Biodosimetry panels and methods

ABSTRACT

The present invention relates to methods and kits to assess an absorbed dose of ionizing radiation and/or the severity of tissue injury from radiation in a patient. The invention also relates to algorithms used to calculate an absorbed dose of radiation based on biomarker measurements of a plurality of biomarkers that are altered relative to a normal control in the event of radiation exposure.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a divisional of U.S. patent application Ser.No. 14/348,275, filed Mar. 28, 2014, which is a 371 of InternationalApplication having Serial No. PCT/US2012/057736, filed Sep. 28, 2012,which claims benefit of U.S. Provisional Application No. 61/540,584filed on Sep. 29, 2011, the contents of all of which are incorporatedherein by reference.

STATEMENT REGARDING FEDERALLY-SPONSORED RESEARCH

This invention was made with federal support under HHSO100201000009Cawarded by the Department of Health and Human Services. The U.S.government has certain rights in the invention.

FIELD OF THE INVENTION

This application relates to assay methods, modules and kits forconducting diagnostic assays useful in the detection of radiationexposure and the severity of tissue injury to radiation.

BACKGROUND OF THE INVENTION

In the aftermath of an incident in which a significant number ofcivilians are exposed to radiation or radioactive materials, healthauthorities will need to be able to rapidly identify individuals whohave been exposed to life-threatening significant doses of radiation.The deadly effects of ionizing radiation (IR) are wide-ranging andinclude systemic and organ-specific damage. Acute effects of high-doseionizing radiation (>2 Gy) include depletion of specific types ofperipheral blood cells, immune suppression, mucosal damage, andpotential injury to other sites such as bone and bone marrow nichecells, gastrointestinal system, lungs, kidneys, and the central nervoussystem. In addition, exposures to low or moderate doses (1-3 Gy) ofionizing radiation can result in increased mortality if accompanied byphysical injuries, opportunistic infections, and/or hemorrhage.Long-term effects include dysfunction or fibrosis in a wide range oforgans and tissues, cataracts, and, ultimately, a higher risk of cancer.In many cases, the effects of radiation exposure can be mitigated byearly triage and treatment.

Although radioactive material can be detected with instruments,assessment of the radiation dose or injury that a person has alreadyreceived is more difficult. Because current and foreseeable medicalcountermeasures for radiation injuries are often expensive,labor-intensive and time-consuming to administer (and monitor), havelimited availability, and are occasionally associated with serioustoxicities, they should only be administered to persons who will likelybenefit from their use. Fast, accurate radiation dose and tissue injuryassessment could greatly facilitate identification of exposed people whocould benefit from early medical intervention.

No rapid diagnostic exists that can reliably discriminate levels of IRexposure based on samples collected at a single time point. The completeblood count, particularly the lymphocyte count, is useful, but optimallyrequires at least two samples spaced hours to days apart to estimatedose. The diagnostic “gold standard” in the field of radiationbiodosimetry, the dicentric chromosome assay, is labor-intensive andslow, and its use in mass-casualty situations would be problematic.

Therefore, there is a need for sensitive and specific biodosimetry doseassessment tools that can be used to identify patients requiring urgentmedical attention, improve risk assessment for the delayed or lateeffects of radiation exposure, improve patient tracking efficiency forrepeated observation or therapeutic administration, and play a role inmonitoring therapy and long-term follow-up. Such tools will also fill animportant need for monitoring radiation received during medical care,for example, radiation received from medical imaging devices, radiationreceived as a medical therapy (for example to treat cancer), orradiation received in preparation for stem-cell transplants. The toolswould provide the capability to detect individuals accidentallyoverexposed, to select individuals and optimize the schedule ofcountermeasure doses used in treatment as well as to monitor theirefficacy for specific individuals.

SUMMARY OF THE INVENTION

The present invention provides a biodosimetry assay panel and methods tomeasure multiple radiation-sensitive protein biomarkers to assessradiation dose and tissue injury. The methods of the present inventioncan be used to triage and guide the treatment of individuals exposed toionizing radiation after a major radiological or nuclear event. Thetools can also be used to guide treatment of individuals exposed toionizing radiation during medical treatment or as a result of anaccidental exposure.

Accordingly, the present invention provides a multiplexed assay kit usedto assess an absorbed dose of ionizing radiation or radiation-inducedtissue injury in a patient sample, wherein the kit is configured tomeasure the level of a plurality of biomarkers in the sample and theplurality of biomarkers includes: (a) a DNA-damage biomarker; (b) aninflammatory-response biomarker; (c) a tissue-damage biomarker; (d) atissue-damage repair biomarker; (e) a hematology-surrogate marker; and(f) combinations thereof. Also contemplated is a device capable ofreceiving a kit such as this or a component thereof to measure level ofsaid plurality of biomarkers, said device operatively associated with acomputer system, said computer system having stored thereon a computerprogram which, when executed by said computer system, causes thecomputer program to perform a method comprising correlating the level ofsaid plurality of biomarkers present in the sample with the dose ofradiation absorbed by the patient.

Also provided is a multiplexed assay kit used to assess an absorbed doseof ionizing radiation in a patient sample, said kit is configured tomeasure the level of a plurality of biomarkers in said sample, saidplurality of biomarkers comprising (i) one or more biomarkers includingFlt-3L, G-CSF, GM-CSF, EPO, CD27, CD45, SAA, CD26, IL-12, TPO, andcombinations thereof; and (ii) an additional biomarker including: (a) aDNA-damage biomarker; (b) an inflammatory-response biomarker; (c) atissue-damage biomarker; (d) a tissue-damage repair biomarker; (e) ahematology-surrogate marker; and (f) combinations thereof. In apreferred embodiment, the plurality of biomarkers comprises Flt-3L,GM-CSF, SAA, TPO, CD27, CD45, CD26, and IL-12.

The invention also includes a method of assessing an absorbed dose ofionizing radiation in a patient sample, said method comprising (a)measuring levels of a plurality of biomarkers in said sample; (b)applying, by a processor, an algorithm to assess said absorbed dose insaid patient based on said levels of said plurality of biomarkers insaid sample; wherein said plurality of biomarkers comprises: (i) aDNA-damage biomarker; (ii) an inflammatory-response biomarker; (iii) atissue-damage biomarker; (iv) a tissue-damage repair biomarker; (v) ahematology-surrogate marker; and (vi) combinations thereof.

In addition, the invention contemplates a method of assessing anabsorbed dose of ionizing radiation in a patient sample, said methodincluding (a) measuring levels of a plurality of biomarkers in saidsample; (b) applying, by a processor, an algorithm to assess saidabsorbed dose in said patient based on said levels of said plurality ofbiomarkers in said sample; said plurality of biomarkers comprising (i)one or more biomarkers comprises Flt-3L, G-CSF, GM-CSF, EPO, CD27, CD45,SAA, CD26, IL-12, TPO, and combinations thereof; and (ii) an additionalbiomarker comprising: (a) a DNA-damage biomarker; (b) aninflammatory-response biomarker; (c) a tissue-damage biomarker; (d) atissue-damage repair biomarker; (e) a hematology-surrogate marker; and(f) combinations thereof. In a preferred embodiment, the plurality ofbiomarkers comprises Flt-3L, GM-CSF, SAA, TPO, CD27, CD45, CD26, andIL-12.

The invention provides a number of multiplexed biodosimetry assay kit(s)used to assess an absorbed dose of ionizing radiation in a patientsample, said kit is configured to measure a level of a plurality ofbiomarkers in said sample, wherein the plurality of biomarkers comprises(a) Flt-3L, G-CSF, GM-CSF, EPO, CD27, CD45, SAA, CD26, IL-12, and/orTPO; and/or (b) Flt-3L, GM-CSF, SAA, TPO, CD27, CD45, CD26, and/orIL-12.

The invention also provides a variety of biodosimetry assay kits used toassess an absorbed dose of ionizing radiation in a patient sample, thekit(s) is (are) configured to measure a level of Flt-3L, G-CSF, GM-CSF,EPO, CD27, CD45, SAA, CD26, IL-12, TPO, and to compare said level(s) toa level of a normal control.

Another embodiment of the invention is a method of assessing an absorbeddose of ionizing radiation in a patient sample, said method comprising

(a) measuring levels of a plurality of biomarkers in said sample;

(b) applying, by a processor, an algorithm to assess said absorbed dosein said patient based on said levels of said plurality of biomarkers insaid sample; said plurality of biomarkers comprising (a) Flt-3L, G-CSF,GM-CSF, EPO, CD27, CD45, SAA, CD26, IL-12, TPO; or (b) Flt-3L, GM-CSF,SAA, TPO, CD27, CD45, CD26, IL-12.

Additionally, the invention includes a method of assessing an absorbeddose of ionizing radiation in a patient sample, said method comprising

(a) measuring a level of Flt-3L, G-CSF, GM-CSF, EPO, CD27, CD45, SAA,CD26, IL-12, TPO in said sample; and

(b) applying, by a processor, an algorithm to assess said absorbed dosein said patient based on said biomarker level in said sample.

Another embodiment of the invention is a method of determining an injuryseverity value comprising

(a) measuring a level of a plurality of biomarkers in a patient sample,wherein one or more biomarkers of said plurality of biomarkers arealtered relative to a normal control in the event of an injury in apatient;

(b) fitting, by a processor, said measured level to a response surfacemodel as a function of an injury severity index and/or time;

(c) computing a cost function for combining said plurality ofbiomarkers; and

(d) identifying an injury severity value that minimizes said costfunction at a known time interval.

In addition, the invention includes a method of determining a radiationdose comprising

(a) measuring a level of a plurality of biomarkers in a patient sample,wherein one or more biomarkers of said plurality of biomarkers arealtered relative to a normal control in the event of radiation exposure;

(b) fitting, by a processor, said measured level to a response surfacemodel as a function of radiation dose or time;

(c) computing a cost function for combining said plurality ofbiomarkers; and

(d) selecting a radiation dose that minimizes said cost function at aknown time interval.

In addition, the invention contemplates a computer readable mediumhaving stored thereon a computer program which, when executed by acomputer system operably connected to an assay system configured tomeasure a level of a plurality of biomarkers in a patient sample, causesthe computer system to perform a method of calculating an injuryseverity value by a method comprising:

(a) fitting said measured level to a response surface model as afunction of a injury severity index or time;

(b) computing a cost function for combining said plurality ofbiomarkers; and

(c) identifying an injury severity value that minimizes said costfunction at a known time interval.

In a further embodiment, the invention includes a computer readablemedium having stored thereon a computer program which, when executed bya computer system operably connected to an assay system configured tomeasure a level of a plurality of biomarkers in a patient sample, causesthe computer system to perform a method of calculating radiation dose bya method comprising:

(a) fitting said measured level to a response surface model as afunction of radiation dose or time;

(b) computing a cost function for combining said plurality ofbiomarkers; and

(c) selecting a radiation dose that minimizes said cost function at aknown time interval.

An additional embodiment includes a multiplexed hematology surrogatebiomarker assay kit configured to measure a level of a plurality ofbiomarkers in a sample, said plurality of biomarkers comprises alymphocyte cell surface marker, a neutrophil cell surface marker, andcombinations thereof.

And a final embodiment of the invention is a method of assayingperipheral blood leukocyte status in a sample comprising

(a) measuring a level of a plurality of hematology surrogate biomarkersin a sample, said plurality of biomarkers comprises a lymphocyte cellsurface marker, a neutrophil cell surface marker, and combinationsthereof;

(b) comparing said level of said biomarkers in said sample to a level ofsaid biomarkers in a normal control sample; and

(c) determining said peripheral blood leukocyte status based on saidcomparison step (b).

BRIEF DESCRIPTION OF THE FIGURES

FIGS. 1(a)-(b) shows the effect of radiation on plasma Flt-3L levels inmice. Panel (a) shows the biomarker level as a function of time afterradiation (⁶⁰Co γ-ray), with each line representing a different dose.Table 8 shows the significance (p values, unpaired t test, highlightingp values<0.01) for the change in the biomarker level for eachirradiation condition, relative to the 0 Gy controls. Panel (b) showsthe results of a Combined Injury Study comparing biomarker levels inmice receiving radiation alone (closed circles) vs. mice receivingradiation combined with a 15% surface area puncture wound (opencircles). Each point on both plots represents the average level from 8replicate animals.

FIGS. 2(a)-(b) shows the effect of radiation on plasma SAA levels inmice. Panel (a) shows the biomarker level as a function of time afterradiation (⁶⁰Co γ-ray), with each line representing a different dose.Table 9 shows the significance (p values, unpaired t test, highlightingp values<0.01) for the change in the biomarker level for eachirradiation condition, relative to the 0 Gy controls. Panel (b) showsthe results of a Combined Injury Study comparing biomarker levels inmice receiving radiation alone (closed circles) vs. mice receivingradiation combined with a 15% surface area puncture wound (opencircles). Each point on both plots represents the average level from 8replicate animals.

FIGS. 3(a)-(b) shows the effect of radiation on plasma G-CSF levels inmice. Panel (a) shows the biomarker level as a function of time afterradiation (⁶⁰Co γ-rays), with each line representing a different dose.Table 10 shows the significance (p values, unpaired t test, highlightingp values<0.01) for the change in the biomarker level for eachirradiation condition, relative to the 0 Gy controls. Panel (b) showsthe results of a Combined Injury Study comparing biomarker levels inmice receiving radiation alone (closed circles) vs. mice receivingradiation combined with a 15% surface area puncture wound (opencircles). Each point on both plots represents the average level from 8replicate animals.

FIGS. 4(a)-(b) shows the effect of radiation on plasma GM-CSF levels inmice. Panel (a) shows the biomarker level as a function of time afterradiation (⁶⁰Co γ-rays), with each line representing a different dose.Table 11 shows the significance (p values, unpaired t test, highlightingp values<0.01) for the change in the biomarker level for eachirradiation condition, relative to the 0 Gy controls. Panel (b) showsthe results of a Combined Injury Study comparing biomarker levels inmice receiving radiation alone (closed circles) vs. mice receivingradiation combined with a 15% surface area puncture wound (opencircles). Each point on both plots represents the average level from 8replicate animals.

FIGS. 5(a)-(b) shows the results of a mouse radiation dose study, and inparticular, the response of plasma IL-6 showing concentration vs time(panel (a)), concentration vs. dose (panel (b)) and the p values forirradiated vs. controls (unpaired t test, highlighting p values<0.01)for the response to each irradiation condition (Table 12).

FIGS. 6(a)-(b) shows the effect of radiation on plasma TPO levels inmice. Panel (a) shows the biomarker level as a function of time afterradiation (⁶⁰Co γ-rays), with each line representing a different dose.Table 13 shows the significance (p values, unpaired t test, highlightingp values<0.01) for the change in the biomarker level for eachirradiation condition, relative to the 0 Gy controls. Panel (b) showsthe results of a Combined Injury Study comparing biomarker levels inmice receiving radiation alone (closed circles) vs. mice receivingradiation combined with a 15% surface area puncture wound (opencircles). Each point on both plots represents the average level from 8replicate animals.

FIGS. 7(a)-(b) shows the effect of radiation on plasma EPO levels inmice. Panel (a) shows the biomarker level as a function of time afterradiation (⁶⁰Co γ-rays), with each line representing a different dose.Table 14 shows the significance (p values, unpaired t test, highlightingp values<0.01) for the change in the biomarker level for eachirradiation condition, relative to the 0 Gy controls. Panel (b) showsthe results of a Combined Injury Study comparing biomarker levels inmice receiving radiation alone (closed circles) vs. mice receivingradiation combined with a 15% surface area puncture wound (opencircles). Each point on both plots represents the average level from 8replicate animals.

FIGS. 8(a)-(b) shows the effect of radiation on plasma IL-5 levels inmice. Panel (a) shows the biomarker level as a function of time afterradiation (⁶⁰Co γ-ray), with each line representing a different dose.Table 15 shows the significance (p values, unpaired t test, highlightingp values<0.01) for the change in the biomarker level for eachirradiation condition, relative to the 0 Gy controls. Panel (b) showsthe results of a Combined Injury Study comparing biomarker levels inmice receiving radiation alone (closed circles) vs. mice receivingradiation combined with a 15% surface area puncture wound (opencircles). Each point on both plots represents the average level from 8replicate animals.

FIGS. 9(a)-(b) shows the results of a mouse radiation dose study, and inparticular, the response of plasma IL-10 showing concentration vs time(panel (a)), concentration vs. dose (panel (b)) and the p values forirradiated vs. controls (unpaired t test, highlighting p values<0.01)for the response to each irradiation condition (Table 16).

FIGS. 10(a)-(b) shows the results of a mouse radiation dose study, andin particular, the response of plasma KC/GRO showing concentration vstime (panel (a)), concentration vs. dose (panel (b)) and the p valuesfor irradiated vs. controls (unpaired t test, highlighting pvalues<0.01) for the response to each irradiation condition.

FIGS. 1 (a)-(b) shows the results of a mouse radiation dose study, andin particular, the response of plasma TNF-α showing concentration vstime (panel (a)), concentration vs. dose (panel (b)) and the p valuesfor irradiated vs. controls (unpaired t test, highlighting pvalues<0.01) for the response to each irradiation condition (Table 17).

FIGS. 12(a)-(b) shows the results of a mouse radiation dose study, andin particular, the response of γ-H2AX in blood cell pellets showingconcentration vs time (panel (a)), concentration vs. dose (panel (b))and the p values for irradiated vs. controls (unpaired t test,highlighting p values<0.01) for the response to each irradiationcondition (Table 19).

FIGS. 13(a)-(b) shows the results of a mouse radiation dose study, andin particular, the response of p53 in blood cell pellets showingconcentration vs time (panel (a)), concentration vs. dose (panel (b))and the p values for irradiated vs. controls (unpaired t test,highlighting p values<0.01) for the response to each irradiationcondition (Table 20).

FIGS. 14(a)-(b) shows the effect of radiation on plasma CD-27 levels inmice. Panel (a) shows the biomarker level as a function of time afterradiation (⁶⁰Co γ-rays), with each line representing a different dose.Table 21 shows the significance (p values, unpaired t test, highlightingp values<0.01) for the change in the biomarker level for eachirradiation condition, relative to the 0 Gy controls. Panel (b) showsthe results of a Combined Injury Study comparing biomarker levels inmice receiving radiation alone (closed circles) vs. mice receivingradiation combined with a 15% surface area puncture wound (opencircles). Each point on both plots represents the average level from 8replicate animals.

FIGS. 15(a)-(b) shows the effect of radiation on plasma IL-12 levels inmice. Panel (a) shows the biomarker level as a function of time afterradiation (⁶⁰Co γ-rays), with each line representing a different dose.Table 22 shows the significance (p values, unpaired t test, highlightingp values<0.01) for the change in the biomarker level for eachirradiation condition, relative to the 0 Gy controls. Panel (b) showsthe results of a Combined Injury Study comparing biomarker levels inmice receiving radiation alone (closed circles) vs. mice receivingradiation combined with a 15% surface area puncture wound (opencircles). Each point on both plots represents the average level from 8replicate animals.

FIGS. 16(a)-(b) shows the effect of radiation on plasma CD45 levels inmice. Panel (a) shows the biomarker level as a function of time afterradiation (⁶⁰Co γ-rays), with each line representing a different dose.Table 23 shows the significance (p values, unpaired t test, highlightingp values<0.01) for the change in the biomarker level for eachirradiation condition, relative to the 0 Gy controls. Panel (b) showsthe results of a Combined Injury Study comparing biomarker levels inmice receiving radiation alone (closed circles) vs. mice receivingradiation combined with a 15% surface area puncture wound (opencircles). Each point on both plots represents the average level from 8replicate animals.

FIGS. 17(a)-(b) shows the effect of radiation on plasma CD26 levels inmice. Panel (a) shows the biomarker level as a function of time afterradiation (⁶⁰Co γ-rays), with each line representing a different dose.Table 24 shows the significance (p values, unpaired t test, highlightingp values<0.01) for the change in the biomarker level for eachirradiation condition, relative to the 0 Gy controls. Panel (b) showsthe results of a Combined Injury Study comparing biomarker levels inmice receiving radiation alone (closed circles) vs. mice receivingradiation combined with a 15% surface area puncture wound (opencircles). Each point on both plots represents the average level from 8replicate animals.

FIGS. 18(a)-(b) are scatter plots showing the predicted dose as afunction of actual dose for the full mouse radiation dose study sampleset using a multi-parameter dose assessment algorithm. The differentcolors correspond to different sampling times. The data are slightlydithered along the X-axis to make the points clearly visible. Points onthe solid lines exactly match the actual dose. Points within the dashedlines are within 1.5 Gy of the actual dose (below 6 Gy) or within 25% ofthe actual dose (above 6 Gy). Panel (a) includes results using theoptimal 5 biomarker panel (Flt-3L, G-CSF, GM-CSF, EPO, IL12/23). Panel(b) includes results using a smaller LPS-insensitive panel (Flt-3L,EPO).

FIGS. 19(a)-(b) are ROC curves for distinguishing 6 Gy doses fromnon-irradiated controls (blue) and for distinguishing 6 Gy doses from 3Gy doses. The ROC curves were generated by varying the value of thepredicted dose used to classify the samples. The histogram inset showsthe distribution of the predicted doses for 6 Gy and non-irradiatedsample sets and demonstrates the separation of the two distributions.Panel (a) includes results using the optimal 5 biomarker panel (Flt-3L,G-CSF, GM-CSF, EPO, IL12/23). Panel (b) includes results using a smallerLPS-insensitive panel (Flt-3L, EPO).

FIGS. 20(a)-(b) show the dose classification accuracy for the mouseBiomarker Discovery data set using a multi-parameter algorithm. Twoperformance metrics are plotted as a function of the number ofbiomarkers used. Each point represents the performance of a differentcombination of biomarkers; all possible combinations of the 12 mostradiation sensitive biomarkers are shown. In Panel (a), accuracy isdisplayed as the percent of samples correctly classified by dose (within1.5 Gy for doses≤6 Gy and within 25% for doses>6 Gy). In Panel (b),prediction error is displayed as the RMS error in the dose predictionacross all samples. Two high-performing biomarker combinations wereselected for each panel size: a panel that did not include injurysensitive markers (yellow data points) and a panel that did includeinjury-sensitive markers (red data points). The performance metrics forthese selected panels are tabulated in Table 8.

FIG. 21 show the performance of the multi-parameter algorithm and theoptimal 6-plex biomarker panel (CD27+Flt-3L+GM-CSF+CD45+IL-12+TPO) forassessing radiation dose in the mouse model. All samples tested in thisstudy were blinded to the individuals conducting the study duringtesting and dose prediction analysis. The plot shows the predicted doseas a function of actual dose for samples collected between 1 and 7 daysafter irradiation. The different colors correspond to the time of samplecollection. The data are slightly dithered along the X-axis to make thepoints clearly visible. Points for which the predicted dose exactlymatches the actual dose will fall on the solid line. Points within thedashed lines meet our dose prediction accuracy criteria and are within1.5 Gy of the actual dose (below 6 Gy) or within 25% of the actual dose(above 6 Gy). The inset shows the percentage of the predicted doses thatfall within our accuracy criteria and the root mean square error in thepredicted doses across the data set.

FIG. 22 shows the performance of the multi-parameter algorithm and theoptimal 6-plex biomarker panel (CD27+Flt-3L+GM-CSF+CD45+IL-12+TPO) forclassifying mouse sample by radiation dose. All samples tested in thisstudy were blinded to the individual conducting the study during testingand dose prediction analysis. The focus of the analysis shown in thisplot is the ability of the algorithm to correctly classify samples aboveor below the critical 2 Gy dose threshold in humans, which is roughlyequivalent to 5 Gy in the mouse model. The plot shows ROC curves fordistinguishing doses≥6 Gy from non-irradiated controls (blue) and fordistinguishing doses≥6 Gy from doses≤3 Gy. The ROC curves were generatedby varying the value of the predicted dose used to classify the samples.The histogram inset shows the distribution of the predicted doses forsamples receiving 0 Gy, 3 Gy or 6 Gy doses and demonstrates theseparation of these distributions. Classification performance at theoptimal predicted dose thresholds is provided in the table below theplot.

FIG. 23 shows the effect of 15% wound injury on the performance of themulti-parameter algorithm and the optimal 6-plex biomarker panel(CD27+Flt-3L+GM-CSF+CD45+IL-12+TPO) for assessing radiation dose in themouse model. The sample set included samples from mice receiving 0 Gy or6 Gy radiations (⁶⁰Co γ-rays) with or without skin wounding (15% surfacearea puncture wound). Samples were collected at different times up to 7days after exposure. There were an equal number of replicates for eachdose/injury/time condition. The plot shows the predicted dose as afunction of actual dose for samples collected between 1 and 7 days afterirradiation. The different colors correspond to the time of samplecollection. ‘Injury’ data points are shown as triangles, ‘No injury’data points are shown as circles. The data are slightly dithered alongthe X-axis to make the points clearly visible. Points for which thepredicted dose exactly matches the actual dose will fall on the solidline. Points within the dashed lines meet our dose prediction accuracycriteria and are within 1.5 Gy of the actual dose (below 6 Gy) or within25% of the actual dose (above 6 Gy). The inset shows the percentage ofthe predicted doses that fall within our accuracy criteria and the rootmean square error in the predicted doses across the data set.

FIGS. 24(a)-(z) show the effect of radiation on plasma biomarkers(Flt-3L, CD20, CD27, TPO, CD177, IL-12, SAA, EPO, G-CSF, salivaryamylase (AMY), CRP, TIMP-1 and TNF-RII, respectively) in Rhesus macaque.Plots show levels as a function of the time after radiation. Panelsshown on the left reflect the results using samples exposed to ⁶⁰Coγ-rays and panels shown on the right reflect the results using samplesexposed to 3 MV LINAC photons. Each point in the plots represents 5 to 6animals for the ⁶⁰Co γ-rays samples, 3 to 4 animals for the 3 MV LINACsamples up to the 9 day time point and 2 animals for the time points>9days. Graphs are linear, with the exception of FIGS. 25(b) and (d) whichis logarithmic.

FIGS. 24(aa)-(dd) show the analysis of CD20, CD177, neutrophils, andlymphocytes in irradiated NHP samples, demonstrating the potential ofthe biomarkers as surrogates radiation-responsive biomarkers in lieu ofblood cell counting. Each graph shows the time course effects ofradiation on the biomarker or biomarker ratio in NHPs (n=6 for each doseand time cohort).

FIGS. 25(a)-(b) show the performance of the multi-parameter algorithmfor classifying NHP samples from sample set (A) by radiation dose. Thedata set was used to train and test the algorithm using a randomsub-sampling approach to avoid training bias. The focus of the analysisshown in this plot is the ability of the algorithm to correctly classifysamples above or below the critical 2 Gy dose threshold in humans, whichis roughly equivalent to 3 Gy in the NHP model. Panel A shows ROC curvesfor distinguishing doses≥3.5 Gy from non-irradiated controls (blue) andfor distinguishing doses≥3.5 Gy from doses≤1 Gy. The ROC curves weregenerated by varying the value of the predicted dose used to classifythe samples. The histogram inset shows the distribution of the predicteddoses for samples receiving 0 Gy, 1 Gy or 3.5 Gy doses and demonstratesthe separation of these distributions. Classification performance at theoptimal predicted dose thresholds is provided in the table below theplot. Panel B shows predicted doses for the NHP samples plotted as afunction of actual dose.

FIG. 26 shows a comparison of biomarker levels in plasma from the normalhuman population and from individuals having high-prevalence chronicdiseases. The data for each group—normal, asthma, high blood pressure(HBP) and rheumatoid arthritis (RA)—are shown in box and whisker formatproviding the median value (center of the box), the lower and upperquartiles (top and bottom of the box) and 1.5 interquartile ranges(whiskers). Outliers are shown as black points directly above or belowthe box and whiskers. Concentrations are provided in pg/mL except forSAA and CRP which are in ng/mL.

FIGS. 27(a)-(b) shows the results of an effort to model the increasedbaseline variation in normal humans relative to non-irradiated mice.Random noise was added to the biomarker levels from the non-irradiatedmice in the Biomarker Discovery data set so that the observed standarddeviations of the data (in log space) matched the observed standarddeviation for the analogous markers in the study of normal human levelsfor the analogous markers measured in the study of normal human levels.Panel A is a histogram comparing the distribution in the baseline levelsof one of the biomarkers (Flt-3L), before and after the addition ofnoise. A table of the observed standard deviations in mice and humansand the standard deviation after noise injection is shown below theplot) Panel B is a scatter plot showing the predicted doses for the 0 Gysamples from the Blinded Study before and after addition of noise. Forcomparison, the predicted doses for the 6 Gy samples are also shown(with no added noise) as is the optimal threshold selected with theoriginal data for classifying samples as 0 Gy or ≥6 Gy (see FIGS.27(a)-(b)). The addition of noise did not result in any additionalmisclassifications and the classification specificity remained at 100%.

FIG. 28 shows the biomarker levels in plasma from melanoma patientsreceiving lymphocyte depleting chemotherapy in preparation forcell-transfer therapy. The study had two arms: one set of patients alsoreceived TBI 3 days after receiving chemotherapy and the other armreceived chemotherapy without TBI. The plot compares biomarker levels insamples from the non-TBI patients (Mela-Cntrl-0 Gy), sample collectedfrom the TBI patients prior to the radiation treatment (Mela-TBI-0 Gy)and samples collected from the TBI patients 5 to 6 hours after receivinga single 2 Gy fraction (Mela-TBI-2 Gy). Biomarker levels from 40 normalblood donors (see FIGS. 27(a)-(b)) are also provided for comparison.Concentrations are provided in pg/mL except for SAA and CRP which are inng/mL. The levels of AMY1A (salivary amylase) and p53 in the patientsreceiving the 2 Gy fraction showed significant elevation (p<0.05)relative to levels prior to exposure or levels in the non-TBI controlarm of the study.

FIGS. 29(a)-(b) show the biomarker levels in plasma from lung cancerpatients (top) and GI cancer patients (bottom) receiving localizedradiation therapy (2 Gy fractions, 5 fractions per week, 6 weeks). Theplot shows biomarker levels prior to radiotherapy and after cumulativedoses of 30 Gy and 60 Gy (lung) or 54 Gy (GI). Biomarker levels from 40normal blood donors (see FIGS. 27(a)-(b)) are also provided forcomparison. Concentrations are provided in pg/mL except for SAA and CRPwhich are in ng/mL.

FIGS. 30(a)-(e) show the results of Confounding Effect mouse study forplasma levels of Flt-3L, SAA, G-CSF, GM-CSF and IL-6. The Y-axis isscaled to make the radiation response visible. In some case the LPSresponse is off-scale. The maximal responses for the differentconditions can be viewed in a log scale in the figure. Results for the 0and 6 Gy conductions from the Radiation Dose Study are plotted side byside for comparison.

FIGS. 31(a)-(e) show the results of Confounding Effect mouse study forplasma levels of TPO, EPO, IL-12/23, IL-5 and IL-10. The Y-axis isscaled to make the radiation response visible. In some case the LPSresponse is off-scale. The maximal responses for the differentconditions can be viewed in a log scale in figure. Results for the 0 and6 Gy conductions from the Radiation Dose Study are plotted side by sidefor comparison.

FIGS. 32(a)-(d) show the results of Confounding Effect mouse study forplasma levels of KC/GRO and TNFα and blood cell pellet levels of p53 andγH2AX. The Y-axis is scaled to make the radiation response visible. Insome case the LPS response is off-scale. The maximal responses for thedifferent conditions can be viewed in a log scale in the figure. Resultsfor the 0 and 6 Gy conductions from the Radiation Dose Study are plottedside by side for comparison.

FIG. 33 provides an overview of the Confounding Effect study. For eachassay, the bar graph presents the average concentration for the controlmice and the average concentration at the conditions that produced themaximal response to G-CSF and LPS over all conditions tested in both theRadiation Dose and Confounding Effect Studies. The signals for eachassay are normalized to the maximal radiation response, which is set at100%.

FIGS. 34(a)-(d) show the results of testing of archived plasma samplesfrom irradiated NHPs for Flt-3L, EPO, CRP and SAA. SAA was measuredusing a commercial ELISA kit. Each point represents the average valuefor three different animals.

FIGS. 35(a)-(d) show the results of testing of archived plasma samplesfrom irradiated NHPs for IL-6, BPI, TPO and p53. Each point representsthe average value for three different animals.

FIGS. 36(a)-(b) show results of testing of archived plasma samples fromirradiated NHPs for the lymphocyte cell surface marker CD20 and theneutrophil cell surface marker CD177. The biomarker concentrations forthe two tested doses (1.0 and 3.5 Gy) are shown as closed circles withthe y-axis scale provided on the left side of the plots. For comparison,lymphocyte and neutrophil cell counts measured on the same animals atthe same times are also shown as open circles, with the y-axis scaleprovided on the right side of the plots. Each point represents theaverage value for three different animals.

FIG. 37 shows the results from a panel of 6 plasma markers (Flt-3L, EPO,p53, CD20, CD177 and SAA) which provides good discrimination of animalsreceiving greater than 3.5 Gy (equivalent to ˜2 Gy in humans) from thosereceiving less than 3.5 Gy and also provided high accuracy forsemi-quantitative dose prediction.

FIGS. 38(a)-(b) show the use of the statistical methods described hereinto generate a severity value or a radiation dose, respectively, from apatient sample by analyzing one or more biomarkers in the same andcorrelating the level(s) of those biomarkers with an injury severityvalue or a radiation dose, respectively. FIG. 38(c) illustrates onenon-limiting example of a system used to analyze a sample using thestatistical methods described herein, wherein the system includes aprocessor and an algorithm module.

DETAILED DESCRIPTION OF THE INVENTION

Unless otherwise defined herein, scientific and technical terms usedherein shall have the meanings that are commonly understood by those ofordinary skill in the art. Further, unless otherwise required bycontext, singular terms shall include pluralities and plural terms shallinclude the singular. The articles “a” and “an” are used herein to referto one or to more than one (i.e., to at least one) of the grammaticalobject of the article. By way of example, “an element” means one elementor more than one element.

One embodiment of the invention is a multiplexed biodosimetry assay kitand methods that can be used to assess an absorbed dose of ionizingradiation in a patient sample. The methods and kits of the invention canbe used to assess the likelihood or risk of developing acute radiationsyndrome (ARS) and/or to assess the clinical severity of ARS in apatient. Depending on the type of radiation exposure, the methods andkits of the present invention may be used to assess radiation dose inany suitable unit of measurement. For example, an absorbed dose ofionizing radiation following external exposure can be measured in avariety of suitable units of measurement, including but not limited toGray (or rad), and sievert (or rem); a dose of radiation after internalcontamination is measured as Committed Effective Dose Equivalent (CEDE);and a dose associated with external and internal exposure is measured asTotal Effective Dose Equivalent (TEDE).

Assay panel(s) employed in certain embodiments of the instant inventioninclude a plurality of radiation biomarkers used to assess radiationexposure. A radiation biomarker can be any substance that acts as anindicator of the exposure of an organism to radiation, including but notlimited to, proteins, nucleic acids, carbohydrates and metabolites. Inone embodiment, the biomarkers included in the panel are all proteins.

Suitable assay panels comprise at least one radiation biomarker from atleast 1, 2, 3, 4 or 5 of the following biomarker classes: DNA-damagebiomarkers, inflammatory-response biomarkers, tissue-damage biomarkers,tissue-damage repair biomarkers, and hematology-surrogate biomarkers. Asused herein, a DNA-damage biomarker is a radiation biomarker associatedwith the host response to radiation induced DNA damage. Aninflammatory-response biomarker is a radiation biomarker that is up- ordown-regulated during a systemic or localized inflammatory responsecaused by radiation exposure. A tissue-damage biomarker is a radiationbiomarker that is released from a tissue as a result of local tissuedamage caused by radiation, whereas a tissue-damage repair biomarker isa protein that is up- or down-regulated during a repair, regeneration,or fibroblastic phase following tissue damage. Tissue-damage repairbiomarkers may also include proteins associated with soft-tissue repairprocesses, including but not limited to fibroblast formation, collagensynthesis, tissue remodeling, and realignment. Finally,hematology-surrogate biomarkers are cell-surface markers for bloodcells, which may be used as surrogates to traditional blood cell counts,for assessing the effect of radiation on specific blood-cellpopulations. Useful hematology surrogate markers include markers foundon general classes of cells (e.g., leukocytes) or more specific celltypes within those classes such as lymphocytes, neutrophils andplatelets or, even more specifically, T-cells or B-cells.

There may be some overlap between the biomarker categories describedabove. For example, some inflammatory response biomarkers may also beassociated with tissue-damage repair. In one embodiment, the radiationbiomarker panel includes at least one inflammatory-response biomarkerand at least one tissue-damage repair biomarker. In an alternateembodiment, the panel includes a biomarker that is both an inflammatoryresponse biomarker and a tissue-damage repair biomarker.

A non-limiting list of biomarkers that can be used in the instantinvention is provided in Table 1 below.

TABLE 1 Biomarker Class Exemplary Biomarkers DNA-damage p53 ATMbiomarker p21 phosphorylated H2AX histone GADD45a (γ-H2AX) Inflammatory-IL-6 IFN response biomarker CRP IL-2 SAA IL-4 IL-1 TNF-alpha IL-5 IL-12IL-10 IL-3 IL12/23 IL-7 KC/GRO Tissue-damage salivary alpha-amylase CKBBbiomarker citrullinated proteins CKMB S100B CKMM SP-D FABP2 BPI GFAP TSPNSE CA15-3 Tissue-damage Flt-3L TPO repair biomarker G-CSF GM-CSF KFGSDF-1a EPO Hematology- CD5 CD26 surrogate biomarker CD-16b CD27 CD20CD40 CD177 CD45

In a preferred embodiment, the assay panel used in the present inventionis configured to measure a level of a plurality of biomarkers in asample, wherein the plurality of biomarkers includes one or more ofFlt-3L, G-CSF, GM-CSF, EPO, CD27, CD45, SAA, CD26, IL-12, TPO, andcombinations thereof, and/or an additional biomarker comprising aDNA-damage biomarker, an inflammatory-response biomarker, atissue-damage biomarker, a tissue-damage repair biomarker, ahematology-surrogate biomarker, and combinations thereof. In a specificembodiment, the panel includes Flt-3L, GM-CSF, SAA, TPO, CD27, CD45,CD26, IL-12, and combinations thereof. In one embodiment, the IL-12assay is specific for the IL-12 p40 subunit in the IL-12 p70 heterodimerand may also cross-react with IL-23 (which also comprises the p40subunit). In another embodiment, the IL-12 assay is specific for thefull IL-12 p70 heterodimer.

The selected biomarkers for assessing exposure to radiation are,preferably, not significantly affected by chronic diseases with highprevalence in the human population, such as diabetes, asthma, high bloodpressure, heart disease, arthritis and/or other chronic inflammatory orautoimmune diseases. The selected biomarkers for assessing exposure toradiation are, preferably, also not affected by other types of trauma(e.g., wounding, burns and/or mental stress) that may also beexperienced by individuals in a radiation event. In one embodiment, thebiomarker response associated with total body radiation exposure (forexample, at 2, 6, 10 or 12 Gy) is less than the biomarker responseassociated with the biomarker response associated with wound, burnand/or mental trauma. Such comparison, may be determined through the useof a combined injury animal model. We note that biomarkers that have asignificant confounding effect from confounding diseases or trauma maystill be selected and have value in a dose assessment algorithm. In oneembodiment, biomarkers that may be affected by such confounding effectsare included, and information about the presence or absence of suchconfounding conditions is included in the algorithm for dose assessment.For example, if a potentially confounding condition is identified in apatient (such as a confounding disease or trauma) approaches that may betaken to minimize the effect of the confounding condition on theaccuracy of a dose assessment algorithm include: i) excluding thepatient from analysis using the algorithm; ii) applying the algorithm,but using a redacted biomarker panel that excludes or applies lessweight to biomarkers that are likely to be affected by the confoundingcondition or iii) applying a different algorithm employing a biomarkerpanel that has been selected to be robust to the confounding condition.

The kits of the present invention can further include devices, reagents,and/or consumables for measuring hematological parameters, such asperipheral blood cell counts, or for measuring “Acute Phase Reaction”(APR) biomarkers. Such assay components can be modifications ofcommercially available products for assessing blood cell counts and APRbiomarkers, such as the Quikread CRP finger-prick device (OrionDiagnostica, Finland) which measures the level of C-reactive protein.

In a preferred embodiment, the invention includes assays forcell-surface markers for lymphocytes and neutrophils which are useful assurrogates for lymphocyte and neutrophil counts. The invention providesa method of conducting a multiplexed hematology-surrogate biomarkerassay and kits therefor, including a kit configured to measure a levelof a plurality of biomarkers in a sample, including lymphocytecell-surface markers and/or neutrophil cell-surface markers. In oneembodiment, the lymphocyte-surface marker comprises CD5, CD20, CD26,CD27, CD40, or combinations thereof. Additionally, the neutrophilcell-surface marker includes CD16b, CD177, or combinations thereof. Thehematology-surrogate biomarker assay methods of the present inventioncan be conducted on a sample comprising whole blood, blood cell pellets,serum, and/or plasma. In one embodiment, the measurements are carriedout in samples prepared by reconstituting dried blood spots. In anotherembodiment, such measurements are carried out using serum and/or plasmasamples. In a preferred embodiment, measurements are carried out usingplasma samples. Surprisingly, we have discovered that the free (i.e.,non-cell bound) forms of neutrophil and lymphocyte surface markers canbe measured in plasma and the levels of these markers in plasma afterradiation exposure provide useful diagnostic information for assessmentof the effects of radiation on neutrophils and lymphocytes.

One of average skill in the art of biological assays will be aware ofnumerous suitable approaches and instrumentation for measuring thebiomarkers and biomarker panels of the invention. In one embodiment, thekit is configured to measure biomarker levels using an immunoassay. In apreferred embodiment, the kit includes a multi-well assay platecomprising a plurality of assay wells configured to measure the level ofsaid plurality of biomarkers in one or more samples. Preferably, thewells are configured to enable the use of individual wells to conductmultiplexed measurements of a plurality of different biomarkers. In onesuch assay plate, a well of the assay plate includes a plurality ofassay domains, at least two of the assay domains comprising reagents formeasuring different biomarkers. In an alternative preferred embodiment,the kit includes an assay cartridge to measure biomarkers in a sample.Preferably, the cartridge comprises a flow cell having an inlet, anoutlet and a detection chamber, said inlet, detecting chamber, andoutlet defining a flow path through said flow cell, said detectionchamber configured to measure said level of said plurality of biomarkersin said sample. Kits used in the present method can further include oneor more additional assay reagents used in an assay and those additionalreagents can be provided in one or more vials, containers, orcompartments of a kit. Moreover, a kit for assessing exposure toradiation can also include (a) a bar-coded patient identification tag;(b) a dried blood spot collection card comprising a bar code that forexample, can be used to facilitate sample identification; (c) a sampletransport bag comprising desiccant; (d) a capillary with a plunger;and/or (e) a lancet.

The samples that can be analyzed in the kits and methods of theinvention include but are not limited to, any biological fluid, cell,tissue, organ and combinations or portions thereof, which includes orpotentially includes a biomarker of a disease, disorder, or abnormalityof interest. For example, a sample can be a histologic section of aspecimen obtained by biopsy, or cells that are placed in or adapted totissue culture. A sample further can be a subcellular fraction orextract, or a crude or substantially pure nucleic acid molecule orprotein preparation. In one embodiment, the samples that are analyzed inthe assays of the present invention are blood or blood fractions suchas, blood pellet, serum, or plasma. Other suitable samples includebiopsy tissue, intestinal mucosa, urine, parotid gland, hematologicaltissues, intestine, liver, pancreas, or nervous system. The sample canbe taken from any patient, including but not limited to animals,mammals, primates, non-human primates, humans, and the like. In oneembodiment, the level is measured using an immunoassay. The radiationbiomarker panels disclosed herein may be used at the onset andthroughout the course of the acute radiation syndrome to assess andmonitor patient health. In a preferred embodiment, a sample is collectedfrom a patient within about 1 to 7 days of radiation exposure.

As used herein, a “biomarker” is a substance that is associated with aparticular biological state, which can be a disease or abnormalcondition. A change in the levels of a biomarker can correlate with therisk or progression of a disease or abnormality or with thesusceptibility of the disease or abnormality to a given treatment. Abiomarker can be useful in the diagnosis of disease risk or the presenceof disease in an individual, or to tailor treatments for the disease inan individual (choices of drug treatment or administration regimes). Inevaluating potential drug therapies, a biomarker can be used as asurrogate for a natural endpoint such as survival or irreversiblemorbidity. If a treatment alters a biomarker that has a directconnection to improved health, the biomarker serves as a “surrogateendpoint” for evaluating clinical benefit.

As used herein, the term “level” refers to the amount, concentration, oractivity of a biomarker. The term “level” can also refer to the rate ofchange of the amount, concentration or activity of a biomarker. A levelcan be represented, for example, by the amount or synthesis rate ofmessenger RNA (mRNA) encoded by a gene, the amount or synthesis rate ofpolypeptide corresponding to a given amino acid sequence encoded by agene, or the amount or synthesis rate of a biochemical form of abiomarker accumulated in a cell, including, for example, the amount ofparticular post-synthetic modifications of a biomarker such as apolypeptide, nucleic acid or small molecule. The term can be used torefer to an absolute amount of a biomarker in a sample or to a relativeamount of the biomarker, including amount or concentration determinedunder steady-state or non-steady-state conditions. Level can also referto an assay signal that correlates with the amount, concentration,activity or rate of change of a biomarker. The level of a biomarker canbe determined relative to a control marker in a sample.

Specific biomarkers valuable in distinguishing between normal anddiseased/exposed patients can be identified by visual inspection of thedata, for example, by visual classification of data plotted on aone-dimensional or multidimensional graph, or by using statisticalmethods such as characterizing the statistically weighted differencebetween control individuals and diseased patients and/or by usingReceiver Operating Characteristic (ROC) curve analysis. A variety ofsuitable methods for identifying useful biomarkers and setting detectionthresholds/algorithms are known in the art and will be apparent to theskilled artisan.

For example and without limitation, diagnostically valuable biomarkerscan be first identified using a statistically weighted differencebetween control individuals and exposed or abnormal patients, calculatedas

$\frac{D - N}{\sqrt{\sigma_{D}*\sigma_{N}}}$wherein D is the median level of a biomarker in patients diagnosed ashaving been exposed to radiation, N is the median (or average) of thecontrol individuals, δ_(D) is the standard deviation of D and σ_(N) isthe standard deviation of N. The larger the magnitude, the greater thestatistical difference between the diseased and normal populations.

According to one embodiment of the invention, biomarkers resulting in astatistically weighted difference between control individuals anddiseased/exposed patients of greater than, e.g., 1, 1.5, 2, 2.5 or 3could be identified as diagnostically valuable markers.

Another method of statistical analysis for identifying biomarkers is theuse of z-scores, e.g., as described in Skates et al. (2007) CancerEpidemiol. Biomarkers Prev. 16(2):334-341.

Another method of statistical analysis that can be useful in theinventive methods of the invention for determining the efficacy ofparticular candidate analytes, such as particular biomarkers, for actingas diagnostic marker(s) is ROC curve analysis. An ROC curve is agraphical approach to looking at the effect of a cut-off criterion,e.g., a cut-off value for a diagnostic indicator such as an assay signalor the level of an analyte in a sample, on the ability of a diagnosticto correctly identify positive or negative samples or subjects. One axisof the ROC curve is the true positive rate (TPR, i.e., the probabilitythat a true positive sample/subject will be correctly identified aspositive, or alternatively, the false negative rate (FNR=1−TPR, theprobability that a true positive sample/subject will be incorrectlyidentified as a negative). The other axis is the true negative rate,i.e., TNR, the probability that a true negative sample will be correctlyidentified as a negative, or alternatively, the false positive rate(FPR=1−TNR, the probability that a true negative sample will beincorrectly identified as positive). The ROC curve is generated usingassay results for a population of samples/subjects by varying thediagnostic cut-off value used to identify samples/subjects as positiveor negative and plotting calculated values of TPR or FNR and TNR or FPRfor each cut-off value. The area under the ROC curve (referred to hereinas the AUC) is one indication of the ability of the diagnostic toseparate positive and negative samples/subjects. In one embodiment, abiomarker provides an AUC≥0.7. In another embodiment, a biomarkerprovides an AUC≥0.8. In another embodiment, a biomarker provides anAUC≥0.9.

Diagnostic indicators analyzed by ROC curve analysis can be a level ofan analyte, e.g., a biomarker, or an assay signal. Alternatively, thediagnostic indicator can be a function of multiple measured values, forexample, a function of the level/assay signal of a plurality ofanalytes, e.g., a plurality of biomarkers, or a function that combinesthe level or assay signal of one or more analytes with a patient'sscoring value that is determined based on visual, radiological and/orhistological evaluation of a patient. The multi-parameter analysis canprovide more accurate diagnosis relative to analysis of a single marker.

Candidates for a multi-analyte panel could be selected by using criteriasuch as individual analyte ROC areas, median difference between groupsnormalized by geometric interquartile range (IQR) etc. The objective isto partition the analyte space to improve separation between groups (forexample, normal and disease populations) or to minimize themisclassification rate.

One approach is to define a panel response as a weighted combination ofthe response for the individual analytes and then compute an objectivefunction like ROC area, product of sensitivity and specificity, etc. Seee.g., WO 2004/058055, as well as US2006/0205012, the disclosures ofwhich are incorporated herein by reference in their entireties. Theweighting coefficients define the partitioning object; for linearcombinations the object is a line in 2 dimensions, a plane in 3dimensions and a hyperplane in higher dimensions. The optimalcoefficients maximize the objective function and can be determined usingalgorithms for finding function extrema in multiple dimensions, e.g.,gradient descent methods, downhill simplex methods, simulated annealingand the like; more details can be found in “Numerical Recipes in C, TheArt of Scientific Computing”, W. Press et al., Cambridge UniversityPress, 1992.

Another approach is to use discriminant analysis, where a multivariateprobability distribution (normal, multinomial etc.) is used to describeeach group. Several distributions result in partitioning hyperplanes inanalyte space. One advantage of this approach is the ability to classifymeasurements into multiple groups (e.g. normal, disease 1, disease 2)simultaneously, rather than two at a time. For further details, see“Principles of Multivariate Analysis, A User's Perspective”, W. J.Krzanowski, Oxford University Press, 2000 and “MultivariateObservations”, G. A. F. Seber, John Wiley, 2004.

Once the partitioning hyperplanes have been determined, the robustnessof different assay panels can be compared by evaluating a distancemetric to the separating hyperplanes for each group. It is noteworthythat the algorithms described above are designed to find the bestclassification between groups; therefore these algorithms can also beused to distinguish between different diseases or populations orsubgroups of the same disease or population. Finally, categorical data(age, gender, race, ethnicity, etc.) can also be coded into differentlevels and used as an optimizing variable in this process.

In one embodiment, the invention provides a radiation dose-calculationalgorithm that includes (a) measuring the levels of a plurality ofradiation biomarkers in a patient sample; (b) fitting said measuredlevels to response surface models for the response of said biomarkers asa function of radiation dose and sample time; (c) computing a costfunction for combining the plurality of biomarkers; (d) selecting acalculated radiation dose and calculated sample time that minimizes thecost function; and optionally, (e) comparing said calculated radiationdose to a threshold value to classify individuals according to dosereceived (for example, to distinguish exposed from non-exposedindividuals or to identify patients who would benefit from a treatmentoption.

Sample time, as used herein, refers to the time between the radiationexposure event and the time at which the sample was collected. Inapplications where the sample time is known or is expected to be known,for example when the exposure occurred in a defined time frame, theactual sample time can be provided to the algorithm. In these cases,only the calculated radiation dose needs to be selected in step (d)above.

In a preferred embodiment, 2-parameter response functions aredetermined, i.e., M_(i) (dose, time), where M_(i) is the expected levelof marker i as a function of dose and sample time. Alternatively, M_(i)may also represent the expected level of a derived value i derived fromthe level of one or more biomarkers, such as the reciprocal of a markerlevel, the log of a marker level, the ratio or product of the levels oftwo markers, etc. Preferably, the response functions are establishedbased on pre-existing data from human, animal or in vitro studies.

Based on these established response functions, one can determine theradiation exposure dose (and, optionally the sample time, if the dose isknown but the sample time is not known) that provides the best overallfit of the different markers (or derived values) to their respectiveresponse functions. One general form of a cost function (F) that can beminimized to find the best fit is provided by the equation below. E_(i)is a function that provides a value associated with the discrepancybetween the measured level of a biomarker (or derived value) i (m_(i))and the expected level of the biomarker (or derived value) predicted bythe response surface for a given dose-time condition (M_(i)(dose,time)).W_(i) is a weighting function that may be dose-dependent and/ortime-dependent for each marker i. The weighting function may be used tovary the importance given to certain markers in certain dose and timeranges. In one embodiment, the weighting is determined based on thestatistical significance of the measurement at that dose and time point.A number of different weighting functions can be used, e.g., the inverseof the coefficient of variation (CV) of the biomarker level at that doseand time. Optionally, the weighting function may be omitted.

${F\left( {{dose},{time}} \right)} = {\sum\limits_{i = 1}^{n}{{W_{i}\left( {{dose},{time}} \right)}{E_{i}\left( {m_{i},{M_{i}\left( {{dose},{time}} \right)}} \right)}}}$

Examples of possible methods for calculating E_(i) include calculatingthe difference, the absolute value of the difference or the square ofthe difference of m_(i) and M_(i). In one embodiment, the values ofm_(i) and M_(i) are normalized to avoid over-emphasizing the moreabundant biomarkers by, for example, dividing them by the minimum,maximum, median or average value for normal samples or for all expectedsamples. One specific example of a preferred cost function with anormalization factor is provided below, which corresponds to a “leastsquares fit” with each term normalized to the product of the measuredvalue and fit value, and scaled by a weighting function.

${F\left( {{dose},{time}} \right)} = {\sum\limits_{i = 1}^{n}{{W_{i}\left( {{dose},{time}} \right)}\frac{\left( {m_{i} - {M_{i}\left( {{dose},{time}} \right)}} \right)^{2}}{m_{i} \times {M_{i}\left( {{dose},{time}} \right)}}}}$

The cost function may also transform the values of M_(i) and m_(i). Inthe preferred cost function described below, log values are used tominimize an over-emphasis on biomarkers with the largest fold-changesand also to minimize bias for biomarkers with higher abundances.

${F({dose})} = {\sum\limits_{i = 1}^{n}{{{Log}\left( \frac{m_{i}}{M_{i}({dose})} \right)}}}$

The measured and fit values for the biomarker levels may also be addedlinearly or in quadrature to the limit of detection (LOD) or lower limitof quantitation to minimize the effect on the cost function of changesin levels near to the detection limit as in the function below.

${{F({dose})} = {\sum\limits_{i = 1}^{n}{{{Log}\left( \frac{m_{i} + {LOD}_{i}}{{M_{i}({dose})} + {LOD}_{i}} \right)}}}},$

wherein m_(i) is the measured value for biomarker i, M_(i) is thepredicted biomarker value as a function of dose at a known timepost-exposure, LOD_(i) is the assay Limit of Detection for biomarker i,and n is the total number of biomarkers being used.

The algorithm described above for assessing radiation dose can also beapplied more generally to disease conditions that can be assessed in theclinical setting using a severity index, i.e., a classification scaleused by clinicians to characterize the stage of a disease or disorder. Avariety of conditions are assessed using severity indices, e.g.,comprising traumatic brain injury, stroke, embolism, liver disease,kidney disease, heart disease, inflammatory bowel disease, Alzheimer'sdisease, dementia, thyroid disease, rheumatoid arthritis, multiplesclerosis, psoriasis, systemic lupus erythematosus, Hashimoto'sthyroiditis, Pernicious anemia, Addison's disease, Type I diabetes,dermatomyositis, Sjogren syndrome, myasthenia gravis, reactivearthritis, Grave's disease, Celiac disease, or cancer.

In this regard, the algorithm described above can be used as an injuryseverity value calculation algorithm that includes (a) measuring a levelof a plurality of biomarkers in a patient sample, wherein one or morebiomarkers of the plurality of biomarkers are altered relative to anormal control in the event of to injury in a patient; (b) fitting themeasured level to a response surface model as a function of an injuryseverity index or time; (c) computing a cost function for combining theplurality of biomarkers; (d) identifying an injury severity value thatminimizes the cost function at a known time interval; and optionally,(e) comparing the injury severity value to a threshold value, wherein aninjury severity value above the threshold value indicates the relativeseverity of the injury. This is illustrated in FIG. 38(a), in which auser (3801) measures a plurality of biomarkers in a patient sample(3802) using an assay system (3803) (including a processor (3804) and analgorithm module (3805), as illustrated in FIG. 38(c)). The processorand algorithm module of the assay system fits the measured biomarkerlevels (3806) to a response surface model (3807), computes a costfunction (3808), and identifies a severity index (3809). Likewise, theuse of the statistical methods to analyze a radiation dose in a sampleis illustrated in FIG. 38(b) in which a radiation dose (3810) isgenerated using the statistical method.

In this embodiment, the cost function is

${{F({SI})} = {\sum\limits_{i = 1}^{n}{{{Log}\left( \frac{m_{i} + {LOD}_{i}}{{M_{i}({SI})} + {LOD}_{i}} \right)}}}},$

wherein m_(i) is the measured value for biomarker i, M_(i) is thepredicted biomarker value as a function of a severity index (SI) at aknown time post-injury, LOD_(i) is the assay limit of detection forbiomarker i, or n is the total number of biomarkers being used.Preferably, the cost function is:

${F({SI})} = {\sum\limits_{i = 1}^{n}{{{{Log}\left( \frac{m_{i}}{M_{i}({SI})} \right)}}.}}$

wherein m_(i) is the measured value for biomarker i, M_(i) is thepredicted biomarker value as a function of a severity index (SI) at aknown time post-injury, or n is the total number of biomarkers beingused.

Other statistical methods can be used to conduct multivariate analysesof biomarker levels. For example, a neural net approach can be used (seee.g., Musavi, et al., Neural Networks (1992) (5): 595-603; Wang, et al.,Artif. Intell. Med. (2010) 48 (2-3): 119-127; Lancashire, L. et al.,Computational Intelligence in Bioinformatics and Computational Biology(2005): pp. 1-6, 14-15). Neural networks are a wide class of flexiblemodels used for simulating nonlinear systems. They consist of an oftenlarge number of “neurons,” i.e. simple linear or nonlinear computingelements, interconnected in often complex ways and often organized intolayers. Other modeling approaches include but are not limited to linearmodels, support vector machines and discriminant analysis (Lancashire etal., “Utilizing Artificial Neural Networks to Elucidate Serum BiomarkerPatterns Which Discriminate Between Clinical Stages in Melanoma,”Proceedings of the 2005 IEEE Symposium on Computational Intelligence inBioinformatics and Computational Biology, Nov. 14-15, 2005, pp. 1-6;Wang et al., “Method of regulatory network that can explore proteinregulations for disease classification,” Artif Intell Med., 48 (2-3)(2010), pp. 119-127).

Therefore, the methods of the present invention can be used to assess anabsorbed dose of ionizing radiation in a patient sample by measuringlevels of a plurality of biomarkers in a sample and applying analgorithm to assess the absorbed dose in the sample based on the levelsof the plurality of biomarker in the samples, wherein the plurality ofbiomarkers comprise a DNA-damage biomarker, an inflammatory-responsebiomarker, a tissue-damage biomarker, a tissue-damage repair biomarker,or a hematology-surrogate biomarker. In a preferred embodiment, thealgorithm quantifies an absorbed dose of ionizing radiation in the rangeof about 1-10 Gy, preferably between about 1-6 Gy, more preferablybetween about 2-6 Gy, or between about 6-10 Gy.

All or one or more parts of the algorithm(s) and statistical method(s)disclosed herein can be performed by or executed on a processor, generalpurpose or special purpose or other such machines, integrated circuitsor by any combination thereof. Moreover, the software instructions forperforming the algorithm(s) and statistical methods(s) disclosed hereinmay also be stored in whole or in part on a computer-readable medium,i.e., a storage device for use by a computer, processor, general orspecial purpose or other such machines, integrated circuits or by anycombination thereof. A non-limiting list of suitable storage devicesincludes but is not limited to a computer hard drive, compact disk,transitory propagating signals, a network, or a portable media device tobe read by an appropriate drive or via an appropriate connection.

In addition to biomarker measurements, biodosimetry assessment canbenefit from additional inputs, such as information regarding clinicalsymptoms. For example, the Biodosimetry Assessment Tool (BAT) is asoftware application that equips healthcare providers with diagnosticinformation (clinical signs and symptoms, physical dosimetry, etc.)relevant to the management of human radiation casualties. Designedprimarily for prompt use after a radiation incident, the softwareapplication facilitates the collection, integration, and archival ofdata obtained from exposed persons. Data collected in templates arecompared with established radiation dose responses, obtained from theliterature, to provide multi-parameter dose assessments. The programarchives clinical information (extent of radioactive contamination,wounds, infection, etc.) useful for casualty management, displaysrelevant diagnostic information in a concise format, and can be used tomanage both military and civilian radiation accidents.

Biomarker levels can be measured using any of a number of techniquesavailable to the person of ordinary skill in the art, e.g., directphysical measurements (e.g., mass spectrometry) or binding assays (e.g.,immunoassays, agglutination assays and immunochromatographic assays).Biomarkers identified herein can be measured by any suitable immunoassaymethod, including but not limited to, ELISA, microsphere-basedimmunoassay methods, lateral flow test strips, antibody based dot blotsor western blots. The method can also comprise measuring a signal thatresults from a chemical reactions, e.g., a change in optical absorbance,a change in fluorescence, the generation of chemiluminescence orelectrochemiluminescence, a change in reflectivity, refractive index orlight scattering, the accumulation or release of detectable labels fromthe surface, the oxidation or reduction or redox species, an electricalcurrent or potential, changes in magnetic fields, etc. Suitabledetection techniques can detect binding events by measuring theparticipation of labeled binding reagents through the measurement of thelabels via their photoluminescence (e.g., via measurement offluorescence, time-resolved fluorescence, evanescent wave fluorescence,up-converting phosphors, multi-photon fluorescence, etc.),chemiluminescence, electrochemiluminescence, light scattering, opticalabsorbance, radioactivity, magnetic fields, enzymatic activity (e.g., bymeasuring enzyme activity through enzymatic reactions that cause changesin optical absorbance or fluorescence or cause the emission ofchemiluminescence). Alternatively, detection techniques can be used thatdo not require the use of labels, e.g., techniques based on measuringmass (e.g., surface acoustic wave measurements), refractive index (e.g.,surface plasmon resonance measurements), or the inherent luminescence ofan analyte.

Binding assays for measuring biomarker levels can use solid phase orhomogenous formats. Suitable assay methods include sandwich orcompetitive binding assays. Examples of sandwich immunoassays aredescribed in U.S. Pat. Nos. 4,168,146 and 4,366,241, both of which areincorporated herein by reference in their entireties. Examples ofcompetitive immunoassays include those disclosed in U.S. Pat. Nos.4,235,601, 4,442,204 and 5,208,535, each of which are incorporatedherein by reference in their entireties.

Multiple biomarkers can be measured using a multiplexed assay format,e.g., multiplexing through the use of binding reagent arrays,multiplexing using spectral discrimination of labels, multiplexing offlow cytometric analysis of binding assays carried out on particles,e.g., using the Luminex® system. Suitable multiplexing methods includearray based binding assays using patterned arrays of immobilizedantibodies directed against the biomarkers of interest. Variousapproaches for conducting multiplexed assays have been described (Seee.g., US 20040022677; US 20050052646; US 20030207290; US 20030113713; US20050142033; and US 20040189311, each of which is incorporated herein byreference in their entireties. One approach to multiplexing bindingassays involves the use of patterned arrays of binding reagents, e.g.,U.S. Pat. Nos. 5,807,522 and 6,110,426; Delehanty J-B., Printingfunctional protein microarrays using piezoelectric capillaries, MethodsMol. Bio. (2004) 278: 135-44; Lue R Y et al., Site-specificimmobilization of biotinylated proteins for protein microarray analysis,Methods Mol. Biol. (2004) 278: 85-100; Lovett, Toxicogenomics:Toxicologists Brace for Genomics Revolution, Science (2000) 289:536-537; Berns A, Cancer: Gene expression in diagnosis, nature (2000),403, 491-92; Walt, Molecular Biology: Bead-based Fiber-Optic Arrays,Science (2000) 287: 451-52 for more details). Another approach involvesthe use of binding reagents coated on beads that can be individuallyidentified and interrogated. See e.g., WO 9926067, which describes theuse of magnetic particles that vary in size to assay multiple analytes;particles belonging to different distinct size ranges are used to assaydifferent analytes. The particles are designed to be distinguished andindividually interrogated by flow cytometry. Vignali has described amultiplex binding assay in which 64 different bead sets ofmicroparticles are employed, each having a uniform and distinctproportion of two dyes (Vignali, D. A A, “Multiplexed Particle-BasedFlow Cytometric Assays” J. ImmunoL Meth. (2000) 243: 243-55). A similarapproach involving a set of 15 different beads of differing size andfluorescence has been disclosed as useful for simultaneous typing ofmultiple pneumococcal serotypes (Park, M. K et al., “A Latex Bead-BasedFlow Cytometric Immunoassay Capable of Simultaneous Typing of MultiplePneumococcal Serotypes (Multibead Assay)” Clin. Diag. Lab ImmunoL (2000)7: 4869). Bishop, J E et al. have described a multiplex sandwich assayfor simultaneous quantification of six human cytokines (Bishop, L E. etal., “Simultaneous Quantification of Six Human Cytokines in a SingleSample Using Microparticle-based Flow Cytometric Technology,” Clin. Chem(1999) 45:1693-1694).

A diagnostic test can be conducted in a single assay chamber, such as asingle well of an assay plate or an assay chamber that is an assaychamber of a cartridge. The assay modules, e.g., assay plates orcartridges or multi-well assay plates), methods and apparatuses forconducting assay measurements suitable for the present invention aredescribed for example, in US 20040022677; US 20050052646; US20050142033; US 20040189311, each of which is incorporated herein byreference in their entireties. Assay plates and plate readers are nowcommercially available (MULTI-SPOT® and MULTI-ARRAY® plates and SECTOR®instruments, MESO SCALE DISCOVERY,® a division of Meso ScaleDiagnostics, LLC, Gaithersburg, Md.).

Reference is made to specific examples illustrating the constructs andmethods above. It is to be understood that the examples are provided toillustrate rather than limit the scope of various embodiments of theinvention.

EXAMPLES

Methods

Assays. Assays were developed as a number of different singleplex ormultiplexed panels in MSD MULTI-ARRAY 96-well plates and analyzed usingECL detection on an MSD plate reader (such as the SECTOR or PR2 lines ofplate readers available from Meso Scale Discovery, a division of MesoScale Diagnostics, LLC, Gaithersburg, Md.). The biomarkers analyzed inthe assay panels include the biomarkers listed in Table 1.

Prior to conducting an assay measurement with an assay panel, the samplewas first diluted in an appropriate sample diluent to the specifieddilution for that panel. The diluted sample (typically about 10 to 25uL) was then combined with an additional volume of sample diluent(typically one to three times the diluted sample volume) in a well of aMULTI-ARRAY assay plate containing an array of capture antibodies forthe targets in the panel. The plate was incubated for about 2 hours withshaking, sample was removed and the wells were washed three times withphosphate buffered saline. A 50-μL volume of a mixture of labeled (MSDSULFO-TAG™ label, an ECL label also available from MESO SCALE DISCOVERY)detection antibodies against the targets in the panel was added and theplate was incubated for about one hour with shaking. The wells werewashed three times with phosphate buffered saline and about 125 μL MSD TRead Buffer (available from MESO SCALE DISCOVERY) was added. The platewas read using an MSD ECL plate reader (available from MESO SCALEDISCOVERY). The reader reports assay signals for each array element inrelative ECL units.

The same procedure was used for analyzing intracellular markers in bloodcell pellets, except that the initial sample was prepared by extractingthe blood cell pellet in a histone extraction buffer (50 mM TRIS pH 7.5,500-mM NaCl, 0.5% NaDeoxycholate, 1% Triton X100, 2-mM EDTA, 1% PhIC, 1%PIC and 1-mM PMSF; 200 μL per 10⁶ white blood cells).

The experimental plate layout contained a negative QC control, apositive QC control and an 8 point calibration curve, all run induplicate. The calibration curve was fit to a 4 parameter logistic(4-PL) fit using 1/y2 weighting and used to calculate sampleconcentrations.

Radiation Dose Studies in Mice. Female mice (strain B6D2F1/J) weresubjected to total body irradiation (TBI) at a range of doses (doses inone study included 0, 1.5, 3, 6, 10 Gy and 14 Gy), using a ⁶⁰Co γ-raysource, at a dose rate of ˜0.6 Gy/min. Mice from these radiation doselevels were sampled at 6 hrs, 1, 2, 3, 5 and 7 days post irradiation(see Table 2). Whole blood was collected at the sampling times andprocessed into a platelet poor EDTA plasma fraction and peripheral bloodleukocyte pellet (PBL). A separate aliquot of whole blood was collectedand used to measure blood cell counts. In a given study, typically 6 to8 mice were tested per dose/time condition. In one study (the “BlindedStudy”), the samples covered the conditions shown in Table 3, but wereprovided for analysis in a blinded fashion to enable an unbiasedcharacterization of the dose-estimation algorithm performance.

Combined Injury Study in Mice. This study examined the effect of adorsal puncture wound covering 15% of total body surface area onradiation biomarker levels (the wound model is described in Ledney etal. 2010). Female mice (strain B6D2F1/J) exposed to 0 (shamirradiation), 3, 6 or 10 Gy were then subject, within an hour ofirradiation, to the 15% surface area puncture wound. Plasma samples werecollected at 6 hrs, 1, 2, 3, 5 or 7 days post irradiation. Irradiationconditions and sample collection were as described above for theBiomarker Discovery Study. As non-injury controls, an equal number ofmice were subject to the same dose/time conditions and processing as theinjured mice, but without receiving the puncture wound. Samples werealso collected from true negative control mice, not subject to shamwounding or irradiation procedures. At least 8 replicate mice weresubjected to each dose/time/wounding condition. Prior to running thefull Combined Injury Study, a smaller Pilot Combined Injury Study wasrun that was limited to the 0 and 6 Gy dose conditions, and only 6replicate mice for each dose/time/injury conditions (see Table 3 andTable 4 for summaries of the test conditions for the Pilot and FullCombined Injury Studies, respectively).

Radiation Dose Testing with NHP Samples. Remnant non-human primate (NHP)samples (Rhesus monkeys—Macaca mulatta) from prior radiation studieswere evaluated as follows. Remnant Sample Set A included EDTA plasmasamples collected pre-irradiation and at various times after TBIirradiation with 0, 1.0, 3.5, 6.5, or 8.5 Gy from a ⁶⁰Co γ-ray sourceand Remnant Sample Set B included EDTA plasma samples collectedpre-irradiation and at various times after TBI irradiation with 7.5,10.0 or 11.5 Gy (6 MV LINAC photon, 0.80 Gy/min). Table 5 summarizes thesamples that were tested in feasibility testing. Sample Set A wasprovided with blood cell counts measured at the time of samplecollection.

Human Samples. To measure the expected normal variation in radiationbiomarkers, remnant platelet-poor EDTA plasma was collected fromindividual blood samples donated at blood donation centers (throughBioreclamation, LLC, Liverpool, N.Y.). Samples were collected from 40normal individuals and up to 10 self-identified individuals for each offour high prevalence chronic diseases (hypertension, diabetes, asthma orrheumatoid arthritis). The samples, summarized in Table 6, were selectedto be diverse in sex, age and race.

To evaluate potential new human models for radiation exposure, remnanthuman samples were collected from radiation oncology patients. EDTAplasma samples were collected from patients receiving standardradiotherapy for lung cancer (15 patients) and GI cancer (8 patients).These treatments involve localized, but relatively large area,irradiation of the affected organ. Typical treatment schedules havepatients receiving 1.8 or 2.0 Gy per day, 5 days per week for 6 weeksfor a total dose of 54 to 60 Gy. Samples were collected prior toirradiation and at the 3 and 6 week time-points. EDTA plasma was alsocollected from melanoma patients (13 patients) receiving TBI inpreparation for cell-transfer therapy (samples collected before and 6hours after receiving a 2 Gy dose). All treatments also includedchemotherapy. More details on the samples and protocols can be found inTable 7.

TABLE 2 Summary of the test groups, and numbers of mice in each testgroup, for the Biomarker Discovery Study. Test Groups Sample CollectionTime (Post Exposure) Dose (Gy) 6 h 1 d 2 d 3 d 5 d 7 d  0 (Sham) 8 8 8 8  8  8  1.5 8 8 8  8  8  8  3 8 8 8  8  8  8  6 8 8 8  8  8  8 10 8 88  8 10 10 14 8 8 8 12 16 20

TABLE 3 Summary of the test groups, and numbers of mice in each testgroup, for the Combined Injury Study. Test Groups Sample Collection Time(Post Exposure) Dose (Gy) Injury 6 h 1 d 2 d 3 d 5 d 7 d 0 (Control) No(Sham) 6 6 6 6 6 6 0 (Control) Yes 6 6 6 6 6 6 6 No (Sham) 6 6 6 6 6 6 6Yes 6 6 6 6 6 6

TABLE 4 Summary of the test groups, and numbers of mice in each testgroup, for the Full Combined Injury Study. Larger numbers or replicatemice were included at higher doses and times to account for mortality inthese groups. Because of the large number of animals the study wasbroken into two sub-studies that were carried out at different times,each sub-study comprising half the animals for each test condition.Sample Collection Time (Post Exposure) Test Groups Dose (Gy) Injury 6 h1 d 2 d 3 d 5 d 7 d 0 (Control) No (Control) 8 8 8  8  8  8 0 (Sham) No(Sham) 8 8 8  8  8  8 0 (Sham) Yes 8 8 8  8  8  8  3 No (Sham) 8 8 8  8 8  8  3 Yes 8 8 8  8  8  8  6 No (Sham) 8 8 8  8  8  8  6 Yes 8 8 8  812 12 10 No (Sham) 8 8 8  8 10 10 10 Yes 8 8 8 12 12 12

TABLE 5 Summary of the remnant NHP samples analyzed in feasibilitytesting. Dose/Time Conditions and Number of Replicates Per Condition forNHP Sample Sets A & B Sample Set A (⁶⁰Co γ-ray) Time Dose (Gy) (Days)0.0 1.0 3.5 6.0 8.5 0 6   6   6   6   5   0.25 4   4   3   NA 6   1 5  5   6   6   6   2 6   6   6   6   6   3 6   6   6   6   6   4 6   6  6   6   6   7 4   6   NA 6   6   8 NA NA NA NA 1   9 6   6   6   6   6  10 NA NA NA NA 5   Sample Set B (6 MV LINAC Photon) Dose (Gy) Time 7.510.0 11.5 0 3   4   3   1 3   4   3   3 3   4   3   5 3   4   3   7 3  4   3   9 4   4   4   13 3   NA NA 17 3   NA NA 21 2   NA NA 25 2   NANA 30 2   NA NA

TABLE 6 (a-b) List of remnant human plasma sample from blood donors usedin testing. The sample set included samples from normal individuals, aswell as from individuals that self-identified as having one of four highprevalence chronic diseases. Normal Individuals (a) Disease Gender AgeRace  1 N M 29 AA  2 N M 39 AA  3 N M 52 AA  4 N F 20 AA  5 N F 36 AA  6N M 39 AA  7 N M 48 C  8 N M 61 C  9 N M 48 C 10 N M 60 C 11 N M 49 C 12N M 51 C 13 N M 47 C 14 N M 37 C 15 N M 29 C 16 N M 42 C 17 N M 49 C 18N M 56 C 19 N M 44 C 20 N M 29 C 21 N F 32 C 22 N F 53 C 23 N F 39 C 24N F 39 C 25 N F 24 C 26 N F 48 C 27 N F 50 C 28 N F 48 C 29 N F 39 C 30N F 46 C 31 N F 66 C 32 N F 22 C 33 N F 52 C 34 N F 23 C 35 N M 52 H 36N M 24 H 37 N M 39 H 38 N M 46 H 39 N F 29 H 40 N F 48 H 41 N F 34 H 42N F 29 H Individuals with High Prevalence Chronic Diseases (b) DiseaseGender Age Race  1 Hy M 43 C  2 Hy M 73 C  3 Hy M 65 C  4 Hy F 56 C  5Hy M 53 AA  6 Hy F 50 AA  7 Hy F 66 H  8 Hy F 54 C  9 Hy F 50 C 10 Hy M44 H 11 As M 75 C 12 As M 41 C 13 As F 40 C 14 As F 21 C 15 As F 46 C 16As M 33 AA 17 As F 24 AA 18 As M 45 H 19 As F 27 H 20 As M 23 C 21 Dia M50 C 22 Dia M 69 C 23 Dia F 48 C 24 Dia M 42 AA 25 Dia F 57 AA 26 Dia M59 H 27 Dia F 59 H 28 Dia M 65 C 29 Dia F 68 C 30 RA F 47 AA 31 RA M 59C 32 RA M 58 M 33 RA M 60 M 34 RA M 59 C 35 RA M 47 C N = Normal; AA =African American; C = Caucasian; H = Hispanic; Hy = Hypertension As =Asthma; Dia = Diabetes; RA = Rheumatoid Arthritis

TABLE 7 (a-c) List of remnant human plasma samples from radiationoncology studies. Brief summaries of the protocols are provided underthe tables. (a) Melanoma patients receiving total body irradiation (TBI)prior to cell-transfer therapy were treated with cyclophosphamide andfludarabine 3-5 days prior to TBI (lymphocyte depletion chemotherapy)and plasma was collected pre-TBI and 6 hours after a single 2 Gyfraction. Melanoma patients 15-30 were in the non-TBI arm of the study.(b) Lung cancer patients receiving radiation to the thorax received 2 Gyfractions, 5 days per week, for 6 weeks, as well as neo-adjuvant and/orconcurrent 5-FU and taxol. Samples were collected pre-radiation andafter total doses of 30 to 60 Gy. (c) GI cancer patients receivingradiation to the GI tracts received 2 Gy fractions, 5 days per week, for6 weeks, as well as concurrent 5-FU or gemcitabine. Samples werecollected pre-radiation and after total doses of 30 and 54 Gy. (a)Melanoma Patients Patient Pre-Rad 2 Gy (TBI)  1 X X  2 X X  3 X X  4 X X 5 X  6 X  7 X  8 X  9 X 10 X X 11 X X 12 X X 13 X X 14 X X 15 X 16 X 17X 18 X 19 X 20 X 21 X 22 X 23 X 24 X 25 X 26 X 27 X 28 X 29 X 30 X Total29 10 (b) Lung Cancer Patients Patient Pre-Rad 30 Gy 60 Gy  1 X X X  2 XX X  3 X X X  4 X X X  5 X X X  6 X X X  7 X X X  8 X X X  9 X X X 10 XX X 11 X X X 12 X X X 13 X X X 14 X X X 15 X X X Total 15 15 15 (c) GICancer Patients Patient Pre-Rad 30 Gy 54 Gy 1 X X X 2 X X 3 X X X 4 X XX 5 X 6 X X X 7 X X X 8 X X X Total 7 7 7

Data Analysis. In evaluating the dose and time responses of individualbiomarkers, the significance of differences in observed responses todifferent test conditions were determined by calculating a p value usinga two-tailed unpaired t-test. The measured dose and time responses ofthe individual biomarkers, as determined in the mouse radiation dosestudy, were used to develop a multi-parameter algorithm for predictingdose. The basic approach is to model the dose and time response for eachbiomarker. To predict dose, the biomarker levels of a patient (or animalmodel) are measured and the dose is calculated that provides the bestcompromise for fitting each biomarker to its response surface model. Inthe studies described herein, it is assumed that the time of exposurewill be known, so only the dose providing the best compromise fit needsto be calculated.

Results

Mouse Radiation Dose and Combined Injury Studies—Individual BiomarkerResponses. The results of biomarker testing for the mouse radiation dosestudy are shown in FIGS. 1(a)-17(b). Each figure includes a plot ofbiomarker levels vs. dose for each collection time, and a plot ofbiomarker levels vs. time for each dose. Each data point for markers inPanels A-D represents the average over 7-8 mice for the controlconditions and 10-12 mice for the sham and irradiated conditions, withthe exception of the EPO, IL-5, IL10, KC/GRO and TNF-α measurements. Thedata points for these assays represent the averages are over 3-4 micefor the controls and 6-8 mice for the sham and irradiation conditions.The figures also provide tables of p values indicating the significancein the change in biomarker levels for each condition, relative to thenon-irradiated control.

Figures are provided for selected biomarkers showing a significantchange (p<0.05) over a range of dose/time conditions. The DNA damage andinflammatory markers were early radiation markers, peaking at 6 hrs or 1day and dropping significantly by 2 days, although there was someevidence that IL-6 and SAA increased at late time points for the higherdoses as acute radiation syndrome progressed. The exceptions were IL-5and IL-12 which showed strong responses at later time points, IL-12being the only marker that showed a strong dose-dependent decrease inconcentration. The biomarkers of tissue-damage repair tended to rise 2days or more after radiation, although G-CSF had a strong early and lateresponse and Flt-3L showed a significant response at all but theearliest (6 h) time point.

FIGS. 1(a)-17(b) also include a plot of biomarker levels vs. time foreach dose and injury condition tested in the Full Combined Injury Study.Focusing on the 0 Gy (non-irradiated) condition and comparing thebiomarker levels for non-injured mice (closed circle) and injured mice(open circle), showed that there were a number of biomarkers that werenot changed by wounding in the absence of radiation exposure (Flt-3L,GM-CSF, TPO, EPO, IL-5 and CD27). SAA, G-CSF and CD-26 by contrast weresignificantly elevated (p<0.05) by wounding, with the effect of woundingbeing larger in magnitude than the effect of radiation exposure. IL-12and CD-45 showed a moderate wounding effect that was statisticallysignificant at some time points, but lower in magnitude than the effectof radiation exposure. The results obtained with the combined injurymodel are roughly consistent with results using an LPS injection model,described herein, except that GM-CSF and IL-12 levels are stronglyelevated in response to LPS, but not affected (GM-CSF) or only weaklyaffected (IL-12) by wounding.

If the biomarkers were insensitive to wounding in non-irradiatedanimals, wounding also did not generally affect biomarker levels afterradiation exposure. The one exception was TPO; wounding appeared toaccelerate the kinetics for the appearance of elevated TPO levels inirradiated animals. The effect was most pronounced for animals receivinga 6 Gy dose. At day 5 post-irradiation the wounded animals had averageTPO levels that were almost 5 times higher than non-wounded animals. Byday 7, the levels for wounded and non-wounded animals were roughlycomparable. We anticipate that use of biomarkers with this type ofeffect should not affect the ability to identify patients who have beenirradiated, but could potentially lead to an overestimation of dose.There is, however, to further improve algorithm accuracy by adjustingthe dose assessment algorithm based on information about wounding orother trauma.

Mouse Radiation Dose Study—Algorithm Development and Testing. Amulti-parameter algorithm (as described in the Methods section) using 5biomarkers (Flt-3L, G-CSF, GM-CSF, EPO, IL12/23) was applied to the fullsample set from a first mouse radiation dose and time study. For eachsample, with its set of biomarker measurements, the predicted dose wascalculated using the repeated random sub-sampling approach (seeMethods). FIG. 18(a) shows a plot of the predicted dose vs. the actualgiven dose for the full set of samples. The dashed lines indicate arange of +/−1.5 Gy from 0 to 6 Gy, and indicate +/−25% above 6 Gy.Accounting for the 2.5-fold higher sensitivity of humans to radiationdose relative to the mouse model (LD50/30 for the B6D2F1/J female mousemodel is ˜9.5 Gy—see, Ledney et al., 2010—vs. ˜3-4 Gy for humans), thecorresponding human equivalent dose ranges would be +/−0.6 Gy from 0 to2.4 Gy, and +/−25% above 2.4 Gy.

The ability of the algorithm to correctly classify doses intoappropriate ranges can be calculated from FIG. 18(a). The percentage ofsamples that fall within the defined boundaries in FIG. 18(a) for alldoses over the time window of 1-7 days after exposure is 90±3%, wherethe standard deviation is the variation in the calculated percentageacross the different combinations of test and training sets. Note thatthe accuracy decreases slightly to 88±3% if the time range is expandedto include the 6 hr time point.

The algorithm was also characterized for its overall ability todiscriminate doses of 6 Gy and above from non-irradiated control mice (0Gy) and to discriminate doses of 6 Gy and above from 3 Gy and below.Given the lower relative survival sensitivity of the mouse model toradiation dose than humans (LD50/30 is roughly 2.5-fold higher for themouse model), this classification should roughly correspond to theability to classify dose above or below about 2 to 3 Gy in humans. FIG.19(a) shows ROC curves generated by varying the predicted dose thresholdused to classify the samples. The ROC curves demonstrate excellentclassification ability with area under curves (AOCs) of 0.999 fordistinguishing 0 Gy from ≥6 Gy and 0.956 for distinguishing ≤3 Gy from≥6 Gy.

Supplemental Mouse Radiation Dose Study—Algorithm Development andTesting. Algorithm development was advanced using results from a secondMouse Radiation Dose Study. To select optimal biomarker panels for usewith our multi-parameter dose-estimation algorithm (as described above),the performance of each possible combination of the 12 most radiationsensitive biomarkers was tested against the Biomarker Discovery Studydata set. A repeated random sub-sampling approach was used so that thetraining and test samples were always independent. Algorithm performancewas calculated using two different metrics: (i) a prediction errormetric provided as the root mean square error (RMSE) in the predicteddoses across the full sample set and (ii) an accuracy metric, where wedefined accuracy to be the percentage of samples for which the predicteddose was within +/−1.5 Gy from 0 to 6 Gy, and +/−25% above 6 Gy.Accounting for the 2.5-fold higher sensitivity of humans to radiationdose relative to the mouse model (LD50/30 for the B6D2F1/J female mousemodel is ˜9.5 Gy—see, Ledney et al., 2010—vs. ˜3-4 Gy for humans), thecorresponding human equivalent dose ranges would be +/−0.6 Gy from 0 to2.4 Gy, and +/−25% above 2.4 Gy.

FIGS. 20(a)-(b) shows the RMSE and Accuracy metrics for the differentpossible biomarkers combinations (each point in the graph represents adifferent combination of 1 to 12 biomarkers). Focusing on the highestperforming combinations for each possible panel size (the top-mostpoints for each panel size), the figure shows that there is no benefitto panel sizes larger than six markers. Table 8 shows the performancemetrics for two top performing panels for each panel: the top performingpanel including one of the three highly wound-sensitive biomarkers (SAA,G-CSF and CD26) and the top performing panel that does not include anyof these three markers. There was no evidence that the injury sensitivemarkers were required for optimal performance, so the six marker panelwithout injury sensitive biomarkers (CD27, Flt-3L, GM-CSF, CD45, IL12and TPO) was selected as the preferred panel for further performancecharacterization.

TABLE 8 The table shows the preferred biomarker panels identified byapplying the multi-parameter dose assessment algorithm to the mouseBiomarker Discovery data set. Two panels are presented for each possiblepanel size. One panel is the highest performing panel, for a given panelsize, that does not include the three most injury sensitive biomarkersas determined by the Combined Injury Study Results: SAA, G-CSF and CD26.The other panel is the highest performing panel that includes one ormore of these injury sensitive markers. The accuracy represents the % ofsamples that were correctly classified by dose (i.e., predicted dosewithin 1.5 Gy of actual dose for doses ≤6Gy or within 25% of the actualdose for doses >6 Gy). The table also provides the root mean squareerror (RMSE) in the predicted dose. The six biomarker panel withoutinjury sensitive biomarkers (CD27 + Flt3L + GM-CSF + CD45 + IL-12 + TPO)was chosen for subsequent algorithm validation work. # Markers PanelAccuracy (%) RMSE (Gy) 1 CD27 84.5 ± 3.0 1.66 ± 0.16 1 Flt-3L 72.7 ± 3.22.74 ± 0.20 2 CD27 + Flt-3L 88.9 ± 2.7 1.46 ± 0.15 2 CD27 + G-CSF* 88.7± 2.5 1.48 ± 0.15 2 CD27 + GM-CSF 88.6 ± 2.4 1.40 ± 0.14 3 CD27 +Flt-3L + GM-CSF 92.9 ± 2.0 1.18 ± 0.12 3 CD27 + IL-12 + G-CSF* 91.9 ±2.4 1.37 ± 0.17 4 CD27 + Flt-3L + GM-CSF + CD26* 93.8 ± 2.0 1.13 ± 0.124 CD27 + Flt-3L + GM-CSF + IL-12 93.7 ± 2.0 1.16 ± 0.15 5 CD27 +Flt-3L + GM-CSF + SAA + CD26* 94.2 ± 2.3 1.09 ± 0.11 5 CD27 + Flt-3L +GM-CSF + CD45 + IL-12 93.6 ± 2.1 1.15 ± 0.14 6 CD27 + Flt-3L + GM-CSF +CD45 + SAA + CD26* 94.7 ± 2.1 1.08 ± 0.11 6 CD27 + Flt-3L + GM-CSF +CD45 + IL-12 + TPO 93.7 ± 2.1 1.14 ± 0.13 7 CD27 + Flt-3L + GM-CSF +CD45 + IL-12 + SAA + CD26* 94.8 ± 1.9 1.06 ± 0.11 7 CD27 + Flt-3L +GM-CSF + CD45 + IL-12 + TPO + CD40 93.1 ± 2.1 1.17 ± 0.13 8 CD27 +Flt-3L + GM-CSF + CD45 + IL-12 + SAA + CD26 + G-CSF* 94.7 ± 2.1 1.09 ±0.12 8 CD27 + Flt-3L + GM-CSF + CD45 + IL-12 + TPO + IL-5 + CD40 92.3 ±2.2 1.21 ± 0.15 9 CD27 + Flt-3L + GM-CSF + CD45 + IL-12 + TPO + IL-5 +SAA + CD26* 94.4 ± 2.0 1.08 ± 0.14 9 CD27 + Flt-3L + GM-CSF + CD45 +IL-12 + TPO + IL-5 + CD40 + EPO 91.4 ± 2.4 1.25 ± 0.16 10 CD27 +Flt-3L + GM-CSF + CD45 + IL-12 + TPO + CD40 + SAA + CD26 + G-CSF* 94.0 ±2.2 1.12 ± 0.12 11 CD27 + Flt-3L + GM-CSF + CD45 + IL-12 + TPO + IL-5 +CD40 + 93.6 ± 2.1 1.14 ± 0.13 SAA + CD26 + G-CSF* 12 CD27 + Flt-3L +GM-CSF + CD45 + IL-12 + TPO + IL-5 + CD40 + EPO + 93.2 ± 2.2 1.14 ± 0.14SAA + CD26 + G-CSF* The * and red font indicate the presence of theinjury sensitive markers G-CSF, SAA and CD26

Performance of Algorithm for Predicting Dose in Blinded Samples. Usingthe full Biomarker Discovery data set as the training set for themulti-parameter algorithm, the optimal 6-biomarker panel was used tocalculate an estimated dose for each sample from the Blinded Study. Inthis analysis, the sampling time information was available and used indose estimation, but the actual dose information was blinded to theanalysts until the dose estimation was complete. FIG. 21 shows thecorrelation of the predicted dose and the actual dose and includesdashed lines that represent our Accuracy criteria as described above.Using this approach, 94.7% of the predicted doses fell within ouraccuracy criteria for all doses over the time window of 1-7 days afterexposure. The RMS error in dose estimation was 1.14 Gy (which should beroughly equivalent to +/−0.46 Gy in humans). FIG. 21 also shows that themajority of the points that fell outside of our accuracy criteria weresamples collected 1 day after radiation, indicating that there may beopportunities for improving performance by identifying additional earlybiomarkers or weighting them more heavily.

The algorithm was also characterized for its overall ability todiscriminate doses of 6 Gy and above from non-irradiated control mice (0Gy) and to discriminate doses of 6 Gy and above from 3 Gy and below.Given the lower sensitivity of the mouse model to radiation than humans(LD50/30 is roughly 2.5-fold higher for the mouse model), thisclassification should roughly correspond to the ability to classifydoses around the critical 2 Gy threshold in humans. FIG. 22 shows ROCcurves generated by varying the predicted dose threshold used toclassify the samples. The ROC curves demonstrate excellentclassification ability with area under curves (AOCs) of 1.000 fordistinguishing 0 Gy from ≥6 Gy and 0.998 for distinguishing ≤3 Gy from≥6 Gy. Using the optimal classifications determined by the ROC analysis,there was perfect separation of 0 Gy and ≥6 Gy samples (100%sensitivity, 100% specificity) and near perfect separation of ≤3 Gy and≥6 Gy samples (99.2% sensitivity and 100% specificity).

Effect of Combined Injury on Algorithm Performance. To provide apreliminary view of the robustness of the algorithm to the potentialconfounding effects of injury, the algorithm (using the selected optimal6-biomarker panel) was used to predict dose in the samples from PilotCombined Injury study (0 and 6 Gy doses, with and without 15% surfacewound). FIG. 23 provides a plot of predicted dose vs. actual dose. Thepercentage of samples that fell within our criteria for dose predictionaccuracy was 93.3%, which is roughly in line with the values that thatwere observed for studies without an injury component (the % accuracyfor the blinded study was 94.7%). The RMSE error in dose (0.85 Gy) islower than the value observed for the blinded study (1.14 Gy), mostlikely because the blinded study included higher doses. Despite theinclusion of injured animals, it was also possible to set aclassification threshold in predicted dose that completely distinguished(100% sensitivity, 100% specificity) the non-irradiated and irradiatedanimals.

Preliminary Testing of Radiation Biomarkers in NHP Samples. Archivedplasma samples from irradiated NHP (Rhesus macaques) were tested withthe NHP biomarker assay panels. For almost all dose/time conditions,however, at least 5 of the 6 replicate samples were tested on allassays.

FIGS. 24(a-z) show the radiation dose responses for 13 biomarkersselected for their radiation sensitivity. FIGS. 24(a-z) show that 5 ofthe 6 biomarkers that were selected for the mouse dose-assessmentalgorithm were either radiation responsive in NHP (Flt-3L, CD27, TPO,and IL-12) and/or had mechanistic analogs that were radiation responsivein NHP (i.e., the neutrophil surface marker CD177 as an analog for CD45in mice and the lymphocyte surface marker CD20 as an analog for CD27 inmice). One marker from the mouse panel, GM-CSF, was not detected incontrol or irradiated NHP; the assay may simply not have sufficientsensitivity for native Rhesus GM-CSF. FIGS. 24(a-z) show that threemarkers that demonstrated radiation sensitivity in the mouse model (SAA,EPO and G-CSF), but that were not selected for use in thedose-assessment algorithm also responded to radiation in the NHP model.FIGS. 24(a-z) show the response of two markers (CRP and salivaryamylase) that are not useful in mice but that have been used to assessradiation exposure in people, and confirms that these biomarkers respondto radiation in the NHP model. Finally, FIGS. 24(a-z) show doseresponses for two novel early responders to radiation (TIMP-1 andTNF-RII) that were identified by screening cancer biomarker panels.TIMP-1 (Tissue Inhibitor of MetalloProteinase 1) is regulates a varietyof physiological processes through the inhibition of metalloproteasesand also has erythroid-potentiating activity. Soluble TNF-RII (solubleTNF receptor II) is released into plasma by proteolytic cleavage of cellbound TNF receptors, and is elevated in a number of inflammatoryconditions.

A first pass examination of using the multi-parameter algorithm for doseassessment in the NHP model was carried using a biomarker panel selectedto roughly correspond to the preferred mouse panel. The panel includedFlt-3L, TPO, IL-12, CD20, CD27, CD177 and salivary amylase. CD20 andCD177 are mechanistically analogous to CD27 and CD45 in the mouse panel.GM-CSF was not included because the NHP GM-CSF assay does not appear tobe sensitive enough to detect native GM-CSF in the plasma from normal orirradiated mice. The one marker without an analog in the mouse panel wassalivary amylase, which is an established marker in NHP, but is notaffected by radiation in mice. The data set gathered from the NHP SampleSet A was analyzed using a slight variation for the algorithm used forthe mouse studies, in which the contribution of the different markerswas weighted based on their dose responsivity in a specific time range.The algorithm was trained and tested on the data set using the randomsub-sampling method to avoid training bias. FIGS. 24(a-z) show theperformance of the algorithm for discriminating animals receiving dosesof 3.5 Gy and above from non-irradiated control animals (0 Gy) and fordiscriminating animals receiving doses of 3.5 Gy and above from thosereceiving 1 Gy and below. Given the lower sensitivity of the NHP modelto radiation than humans (LD50/30 is roughly 1.5-fold higher for themouse model), this classification should roughly correspond to theability to classify doses around the critical 2 Gy threshold in humans(3 Gy in NHP). FIGS. 25(a)-(b) shows ROC curves generated by varying thepredicted dose threshold used to classify the samples. The ROC curvesdemonstrate excellent classification ability with area under curves(AOCs) of 1.000 for distinguishing 0 Gy from ≥3.5 Gy and 0.995 fordistinguishing ≤1 Gy from ≥3.5 Gy. Using the optimal classificationsdetermined by the ROC analysis, there was perfect separation of 0 Gy and≥3.5 Gy samples (100% sensitivity, 100% specificity) and near perfectseparation of ≤1 Gy and ≥3.5 Gy samples (96.9% sensitivity and 98.5%specificity). FIGS. 25(a)-(b) also shows the correlation of predicteddose and actual dose and shows that the correlation is very good nearthe critical 3 Gy point.

Human Normal and Diseased Samples. The human biomarker panels weretested with a set of remnant plasma samples from blood donors (Table 6)that included 42 normal healthy individuals as well as samples fromdonors self-reporting as suffering from one of four high-prevalencechronic diseases: hypertension (10 samples), rheumatoid arthritis (6samples), asthma (10 samples) and diabetes (9 samples). The results areplotted in bar and whisker format in FIG. 26. There was no evidence thatthe disease populations were significantly different (p<0.05) than thenormal population for any of the biomarkers.

When judging the ability of the mouse model data to support the use of adose-assessment algorithm in humans, one consideration is whether theincreased normal range one would expect for biomarkers in a diversehuman population (relative to an inbred mouse strain) would increase thelikelihood for false positives. We decided to study this problem byadding random noise to the measured biomarker levels from thenon-irradiated mice in the Biomarker Discovery Study, so that thevariability in the “normal” mouse levels matched the observedvariability in the normal human population. We applied this noise to 4of the biomarkers in the preferred 6-biomarker panel (Flt-3L, CD27,GM-CSF, and IL-12). CD45 was not measured in the human sample set, sothere was no reference for comparison. TPO actually showed lowervariation in the human sample set than in the mouse sample set, so themouse levels were left unchanged.

The data was then analyzed to determine how the added noise affected thespecificity by which non-irradiated control mice could be distinguishedfrom mice exposed to 6 Gy. The classification as 0 Gy or ≥6 Gy wascarried out using the optimal threshold selected in the absence ofinjected noise. As shown in FIGS. 27(a)-(b), increasing the baselinebiomarker variability did not cause any additional misclassificationsand the measured specificity for classification remained at 100%.

Human Samples from Patients Receiving Radiation Oncology. Sample setsfrom cancer patients receiving radiation (Table 7) were evaluated aspotential models for assessing biodosimetry algorithms. One set ofsamples were from melanoma patients receiving lymphocyte depletingchemotherapy prior to cell-transfer therapy. This study included one armthat also received total body irradiation (3 days after chemotherapy)and one arm that did not. Samples were only available pre-irradiationand 5 to 6 hours after the first 2 Gy fraction, so the sample set wasrelevant for early onset biomarkers. The results shown in FIG. 28indicate that the biomarker levels in the pre-radiation draws and inpatients in the non-TBI arm could be substantially different from thenormal range due to the chemotherapy regimen. Nevertheless, twobiomarkers showed up as significantly elevated in the post-irradiationgroup relative to the pre-irradiation group and the non-TBI arm:salivary amylase (p=0.0012), a well-known early onset radiation markerand p53 (p=0.013), a marker identified as an early (<1 day) marker.

Samples were also tested from patients receiving localized radiation forlung of GI cancer (2 Gy per day, 5 times per week, 6 weeks), incombination with neo-adjuvant or concurrent chemotherapy. Biomarkerlevels were measured in samples taken pre-radiation and after cumulativedoses of 30 and 54 to 60 Gy (FIGS. 29(a)-(b)). Overall, there were nosignificant observed changes in biomarker levels as a result of theselocalized radiation therapies. There was evidence for small increases inaverage levels of hematopoietic markers (Flt-3L and TPO) and small dropsin levels of soluble lymphocyte surface markers (CD5 and CD20), but thechanges were moderate (˜a factor of 2) and there was considerableoverlap between the distributions.

Mouse Confounding Effect Study—Individual Biomarker Responses. Theresults of the preliminary mouse confounding effect study are providedin FIGS. 30(a)-32(d) for the biomarkers showing radiation sensitivity.The figures provide biomarker levels vs. collection time in the absenceof confounding factors or after injection of LPS or G-CSF. Plots areprovided for both un-irradiated mice and mice exposed to 6 Gy at 2 hoursafter treatment with the confounding factor. The preliminary confoundingeffect study used a different radiation source (X-ray) than theradiation dose-response study (γ-ray), so the 0 and 6 Gy conditions fromthe radiation dose study are overlaid to gauge consistency of resultsbetween the studies. FIG. 33 provides bar graphs showing the relativemagnitudes of the maximal observed LPS and G-CSF responses relative tothe maximal observed radiation response (including samples from theRadiation Response study) and allows for a rapid assessment of whichmarkers can be subject to confounding effects.

FIG. 33 shows that several assays (SAA, G-CSF, GM-CSF, IL-6, IL-12,IL-12/23 and KC/GRO) showed an LPS response on the same scale or greaterthan the maximal radiation response. These assays were primarilycytokines or inflammatory-response markers. Flt-3L, EPO, TPO, p53 andγH2AX, in contrast, were insensitive or showed only slight elevations inresponse to LPS. Interestingly, TNF-α has little or no radiationresponse but responds strongly to LPS, and can have utility foridentifying samples with significant inflammation. Most markers showedno response to G-CSF treatment. Even when a response was observed, forexample EPO, the response was relatively small on the scale of theradiation response.

Mouse Confounding Effect Study—Algorithm with Reduced LPS InsensitiveBiomarker Set. One approach to address the confounding effects ofnon-radiation related inflammatory responses is to remove theinflammatory biomarkers from the dose assessment algorithm for patientswith obvious trauma or infections. Algorithm performance wascharacterized after removing the LPS-sensitive biomarkers from thepreferred 5 panel biomarker set to produce a 2 biomarker LPS-insensitivepanel (Flt-3L and G-CSF). FIGS. 18(b) and 19(b) provide plots ofpredicted dose vs. actual dose and ROC curves for classifying sampleswith dose≥6 Gy, that were produced analogously to the curves provide inFIGS. 18(a) and 19(a) for the full 5 marker panel. The reduced panel wasstill useful for predicting dose, although there was some degradation inperformance relative to the full panel. The accuracy for classifyingdoses within 1.5 Gy for doses up to 6 Gy or within 25% for doses greaterthan 6 Gy was 74±4%, compared to 91±3% for the full panel. The AUC forthe ROC curve for distinguishing doses≥6 Gy from non-irradiated controlswas 0.989, compared to 0.999 for the full panel. The AUC fordistinguishing doses≥6 Gy from doses≤6 Gy was 0.882, compared to 0.956for the full panel.

Preliminary Testing of NHP Samples. FIGS. 34(a)-35(d) show the resultsobtained based on preliminary testing of a subset of the Rhesus plasmasamples collected between 0 and 9 days after TBI with 1 or 3.5 Gy (3animals per dose). The results largely confirm those seen with the mousemodel; the differences are highlighted below. The NHP model, generally,showed higher radiation sensitivity with stronger responses at 3 Gy andmany markers providing good responses at 1 Gy. EPO and SAA respondedover a broader time range (1-9 days for EPO. 1-3 days for SAA) than wasobserved in mice. CRP, while being non-radiation responsive in mice,provided a strong early response in NHP. BPI and p53 were both observedas very early (6 h) markers. GM-CSF did not respond to radiation in theNHP model (data not shown).

FIGS. 36(a)-(b) shows interesting results obtained when the plasmasamples were tested with the CD20 (lymphocyte) and CD177 (neutrophil)surrogate markers. Preliminary testing with a small set of archivedblood pellet samples collected 0, 1 or 2 days after irradiation showedthat the CD20 and CD177 levels showed a similar drop over time asobserved by cell counting (data not shown). More interestingly, FIGS.36(a)-(b) shows, for the plasma samples from the 1 and 3.5 Gy cohort,that there are measurable levels of free (not cell-bound) CD-20 andCD-177 in the plasma and that the dose- and time-course changes in theplasma levels of these markers exhibit useful diagnostic information.

Additional NHP sample testing and development of dose assessmentalgorithm for the NHP model. Using data from the NHP model, the approachof assessing radiation dose by fitting multiplexed biomarker data totime-dose response surfaces for each biomarker was evaluated (the sameapproach described above for the mouse data). The results showed that apanel of 6 plasma markers (Flt-3L, EPO, p53, CD20, CD177 and SAA) canprovide good discrimination of animals receiving greater than 3.5 Gy(equivalent to ˜2 Gy in humans) from those receiving less than 3.5 Gyand also provided high accuracy for semi-quantitative dose prediction.The results for these 6 biomarkers are shown in Table 26 below and inFIG. 37. Table 26 also illustrates the discrimination utility ofadditional biomarkers measured in this study.

TABLE 26 # Markers Panel % Accuracy RMSE (Gy) 1 CD20 64.8 1.51 1 SAA63.5 1.57 1 Flt-3L 69.6 1.44 1 CD177 45.5 2.85 2 p21 + CD20 69.1 0.97 2Flt-3L + CD20 69.1 1.04 2 CD20 + CD177 66.4 1.01 3 TPO + p21 + CD20 96.50.73 3 Flt-3L + EPO + CD20 92.0 0.87 4 Flt-3L + EPO + CD20 + SAA 94.20.58 4 Flt-3L + EPO + CD20 + p53 94.2 0.69 5 Flt-3L + EPO + CD20 + 97.10.44 CD177 + SAA 5 Flt-3L + EPO + CD20 + 96.4 0.49 CD177 + CRP 6Flt-3L + EPO + CD20 + 97.5 0.35 CD177 + p53 + SAA 7 Flt-3L + EPO +CD20 + 96.5 0.41 CD177 + CRP + p53 + p21 8 Flt-3L + EPO + CD20 + 99.30.34 CD177 + CRP + p53 + p21 + TPO 9 Flt-3L + EPO + CD20 + 96.6 0.42CD177 + CRP + p53 + p21 + TPO + Amylase 10 Flt-3L + EPO + CD20 + 97.10.42 CD177 + CRP + p53 + p21 + TPO + Amylase + SAA

The impact of replacing the individual values of CD20 and CD177 in themodel with the ratio of CD177/CD20 was evaluated in order to determineif this change would improve the accuracy of the algorithm (analogous tothe use of neutrophil/lymphocyte ratio for dose assessment based onhematology results). As shown in Table 27 (a)-(b), the use of the ratioprovided roughly equivalent accuracy to the use of the individualvalues.

TABLE 27 (a)-(b) X = CD177 + X = CD177/ CD20 CD20 # of Best PanelIncluding X, % % Markers When X = CD177 + CD20 Correct RMSE Correct RMSE2 X 88 1.17 75 1.41 3 X + SAA 91 1.01 86 1.41 4 X + Flt-3L + CRP 94 0.8089 1.16 5 X + Flt-3L + EPO + 97 0.44 92 0.77 SAA 6 X + Flt-3L + EPO + 980.35 94 0.51 p53 + SAA 7 X + Flt-3L + EPO + 99 0.41 94 0.49 CRP + p53 +p21 8 X + Flt-3L + EPO + 99 0.34 96 0.42 CRP + p53 + p21 + TPO 9 X +Flt-3L + EPO + 99 0.42 96 0.50 CRP + p53 + p21 + TPO + Amylase 10 X +Flt-3L + EPO + 97 0.42 97 0.41 CRP + p53 + p21 + 2 X 88 1.17 75 1.41 3X + SAA 91 1.01 86 1.41 4 X + p21 + SAA 92 0.84 91 1.12 5 X + Flt-3L +p21 + 94 0.72 93 0.77 SAA 6 X + Flt-3L + CRP + 95 0.65 95 0.65 p21 + SAA7 X + Flt-3L + EPO + 97 0.45 97 0.42 CRP + p21 + SAA 8 X + Flt-3L +EPO + 98 0.34 98 0.34 CRP + TPO + p53 + SAA 9 X + Flt-3L + EPO + 97 0.4097 0.37 CRP + TPO + p21 + p53 + SAA 10 X + Flt-3L + EPO + 97 0.42 970.41 CRP + TPO + p21 + p53 + SAA + Amylase

Alternate algorithms for dose assessment were also evaluated. A simplelinear model (Dose=A₁C₁+A₂C₂+A₃C₃+ . . . , where Ci is the concentrationof marker i and Ai is an empirically determined coefficient) wasequivalent to the response surface based model for distinguishinganimals exposed to 0 and 3.5 Gy (both models were able to correctlyclassify all samples from animals in these two groups). The linear modelhas the advantage of not requiring knowledge of the time betweenexposure and sample collection, but does not quantify dose as well asthe response surface model (root mean square error for dose assessmentfor the optimal panel was ˜1 Gy vs. ˜0.3 Gy for the response surfacemodel).

* * *

Various publications and test methods are cited herein, the disclosuresof which are incorporated herein by reference in their entireties, Incases where the present specification and a document incorporated byreference and/or referred to herein include conflicting disclosure,and/or inconsistent use of terminology, and/or theincorporated/referenced documents use or define terms differently thanthey are used or defined in the present specification, the presentspecification shall control.

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What is claimed is:
 1. A method of assessing an absorbed dose ofionizing radiation in a plasma or blood sample from a patient, saidmethod comprising (a) measuring the level of each of at least sixmarkers in said sample, wherein said at least six markers comprise CD20,Flt-3L, CD177, AMY, IL-12, and TPO; (b) applying, by a processor, analgorithm to assess said absorbed dose in said patient as being above orbelow a 2 Gray (Gy) threshold based on said level of each of said atleast six markers in said sample over time with a sensitivity of 96.9%or higher and a specificity of 98.5% or higher; (c) grouping the patientinto one of two groups, a first group being a group having an assessedabsorbed dose below the threshold of 2 Gy, a second group being a grouphaving an assessed absorbed dose above the threshold of 2 Gy; and (d)administering a medical countermeasure to the patient if the patient isgrouped in the second group.
 2. The method of claim 1 wherein saidalgorithm quantifies an absorbed dose of ionizing radiation in the rangeof about 1-10 Gy.
 3. The method of claim 1 wherein the sample comprisesplasma.
 4. The method of claim 1, wherein the sample comprises blood. 5.The method of claim 1, wherein said levels of said at least six markersare measured using an immunoassay.
 6. The method of claim 5, wherein theimmunoassay is conducted in a multi-well assay plate or in a cartridge.7. The method of claim 6, wherein the immunoassay is conducted in thecartridge.